Avoiding long-haul air travel during the COVID-19 pandemic

October 28th, 2020
by jodi

I would not recommend long-haul air travel at this time.

An epidemiological study of a 7.5 hour flight from the Middle East to Ireland concluded that 4 groups (13 people), traveling from 3 continents in four groups, who used separate airport lounges, were likely infected in flight. The flight had 17% occupancy (49 passengers/283 seats; 12 crew) and took place in summer 2020.
(Note: I am not an epidemiologist.)

The study (published open access):
Murphy Nicola, Boland Máirín, Bambury Niamh, Fitzgerald Margaret, Comerford Liz, Dever Niamh, O’Sullivan Margaret B, Petty-Saphon Naomi, Kiernan Regina, Jensen Mette, O’Connor Lois. A large national outbreak of COVID-19 linked to air travel, Ireland, summer 2020. Euro Surveill. 2020;25(42):pii=2001624. https://doi.org/10.2807/1560-7917.ES.2020.25.42.2001624

Irish news sites including RTE and the Irish Times also covered the paper.

Figure 2 from
Figure 2 from “A large national outbreak of COVID-19 linked to air travel, Ireland, summer 2020” https://doi.org/10.2807/1560-7917.ES.2020.25.42.2001624

Caption in original “Passenger seating diagram on flight, Ireland, summer 2020 (n=49 passengers)”
“Numbers on the seats indicate the Flight Groups 1–4.”


The age of the 13 flight cases ranged from 1 to 65 years with a median age of 23 years. Twelve of 13 flight cases and almost three quarters (34/46) of the non-flight cases were symptomatic. After the flight, the earliest onset of symptoms occurred 2 days after arrival, and the latest case in the entire outbreak occurred 17 days after the flight. Of 12 symptomatic flight cases, symptoms reported included cough (n = 7), coryza (n = 7), fever (n = 6) and sore throat (n = 5), and six reported loss of taste or smell. No symptoms were reported for one flight case. A mask was worn during the flight by nine flight cases, not worn by one (a child), and unknown for three.

Murphy Nicola, Boland Máirín, Bambury Niamh, Fitzgerald Margaret, Comerford Liz, Dever Niamh, O’Sullivan Margaret B, Petty-Saphon Naomi, Kiernan Regina, Jensen Mette, O’Connor Lois. A large national outbreak of COVID-19 linked to air travel, Ireland, summer 2020. Euro Surveill. 2020;25(42):pii=2001624. https://doi.org/10.2807/1560-7917.ES.2020.25.42.2001624 (Notes to Figure 1 Caption)

“It is interesting that four of the flight cases were not seated next to any other positive case, had no contact in the transit lounge, wore face masks in-flight and would not be deemed close contacts under current guidance from the European Centre for Disease Prevention and Control (ECDC) [1].”

Murphy Nicola, Boland Máirín, Bambury Niamh, Fitzgerald Margaret, Comerford Liz, Dever Niamh, O’Sullivan Margaret B, Petty-Saphon Naomi, Kiernan Regina, Jensen Mette, O’Connor Lois. A large national outbreak of COVID-19 linked to air travel, Ireland, summer 2020. Euro Surveill. 2020;25(42):pii=2001624. https://doi.org/10.2807/1560-7917.ES.2020.25.42.2001624

“The source case is not known. The first two cases in Group 1 became symptomatic within 48 h of the flight, and COVID-19 was confirmed in three, including an asymptomatic case from this Group in Region A within 5 days of the flight. Thirteen secondary cases and one tertiary case were later linked to these cases. Two cases from Flight Group 2 were notified separately in Region A with one subsequent secondary family case, followed by three further flight cases notified from Region B in two separate family units (Flight Groups 1 and 2). These eight cases had commenced their journey from the same continent and had some social contact before the flight. The close family member of a Group 2 case seated next to the case had tested positive abroad 3 weeks before, and negative after the flight. Flight Group 3 was a household group of which three cases were notified in Region C and one case in Region D. These cases had no social or airport lounge link with Groups 1 or 2 pre-flight and were not seated within two rows of them. Their journey origin was from a different continent. A further case (Flight Group 4) had started the journey from a third continent, had no social or lounge association with other cases and was eated in the same row as passengers from Group 1. Three household contacts and a visitor of Flight Group 4 became confirmed cases. One affected contact travelled to Region E, staying in shared accommodation with 34 others; 25 of these 34 became cases (attack rate 73%) notified in regions A, B, C, D, E and F, with two cases of quaternary spread.”

Murphy Nicola, Boland Máirín, Bambury Niamh, Fitzgerald Margaret, Comerford Liz, Dever Niamh, O’Sullivan Margaret B, Petty-Saphon Naomi, Kiernan Regina, Jensen Mette, O’Connor Lois. A large national outbreak of COVID-19 linked to air travel, Ireland, summer 2020. Euro Surveill. 2020;25(42):pii=2001624. https://doi.org/10.2807/1560-7917.ES.2020.25.42.2001624

“In-flight transmission is a plausible exposure for cases in Group 1 and Group 2 given seating arrangements and onset dates. One case could hypothetically have acquired the virus as a close household contact of a previous positive case, with confirmed case onset date less than two incubation periods before the flight, and symptom onset in the flight case was 48 h after the flight. In-flight transmission was the only common exposure for four other cases (Flight Groups 3 and 4) with date of onset within four days of the flight in all but the possible tertiary case. This case from Group 3 developed symptoms nine days after the flight and so may have acquired the infection in-flight or possibly after the flight through transmission within the household.”

Murphy Nicola, Boland Máirín, Bambury Niamh, Fitzgerald Margaret, Comerford Liz, Dever Niamh, O’Sullivan Margaret B, Petty-Saphon Naomi, Kiernan Regina, Jensen Mette, O’Connor Lois. A large national outbreak of COVID-19 linked to air travel, Ireland, summer 2020. Euro Surveill. 2020;25(42):pii=2001624. https://doi.org/10.2807/1560-7917.ES.2020.25.42.2001624

“Genomic sequencing for cases travelling from three different continents strongly supports the epidemiological transmission hypothesis of a point source for this outbreak. The ability of genomics to resolve transmission events may increase as the virus evolves and accumulates greater diversity [23].”

Murphy Nicola, Boland Máirín, Bambury Niamh, Fitzgerald Margaret, Comerford Liz, Dever Niamh, O’Sullivan Margaret B, Petty-Saphon Naomi, Kiernan Regina, Jensen Mette, O’Connor Lois. A large national outbreak of COVID-19 linked to air travel, Ireland, summer 2020. Euro Surveill. 2020;25(42):pii=2001624. https://doi.org/10.2807/1560-7917.ES.2020.25.42.2001624

Authors note that a large percentage of the flight passengers were infected:

“We calculated high attack rates, ranging plausibly from 9.8 % to 17.8% despite low flight occupancy and lack of passenger proximity on-board.”

Murphy Nicola, Boland Máirín, Bambury Niamh, Fitzgerald Margaret, Comerford Liz, Dever Niamh, O’Sullivan Margaret B, Petty-Saphon Naomi, Kiernan Regina, Jensen Mette, O’Connor Lois. A large national outbreak of COVID-19 linked to air travel, Ireland, summer 2020. Euro Surveill. 2020;25(42):pii=2001624. https://doi.org/10.2807/1560-7917.ES.2020.25.42.2001624

Among the reasons for the uncertainty of this range is that “11 flight passengers could not be contacted and were consequently not tested.” (A twelfth passenger “declined testing”.) There is also some inherent uncertainty due to incubation period and possibility of “transmission within the household”, especially after the flight; authors note that “Exposure possibilities for flight cases include in-flight, during overnight transfer/pre-flight or unknown acquisition before the flight.”

Beyond the 13 people on the flight, cases spread to several social groups, across “six of the eight different health regions (Regions A–H) throughout the Republic of Ireland”. Flight groups 1 and 2 started their travel from one continent; Flight group 3 from another; Flight group 4 from a third continent.

Figure 3 from
Figure 3 from “A large national outbreak of COVID-19 linked to air travel, Ireland, summer 2020” https://doi.org/10.2807/1560-7917.ES.2020.25.42.2001624
caption in original: “Diagram of chains of transmission, flight-related COVID-19 cases, Ireland, summer 2020 (n=59)”

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Paid Undergraduate Research position at UIUC for Fall & Spring 2020

August 18th, 2020
by jodi

University of Illinois undergraduates are encouraged to apply for a position in my lab. I particularly welcome applications from students in the new iSchool BS/IS degree or in the university-wide informatics minor. While I only have 1 paid position open, I also supervise unpaid independent study projects.


Dr. Jodi Schneider and the Information Quality Lab <https://infoqualitylab.org> seek undergraduate research assistants for 100% REMOTE WORK. Past students have published research articles, presented posters, earned independent study credit, James Scholar research credit, etc.

One paid position in news analytics/data science for Assessing the Impact of Media Polarization on Public Health Emergencies, funded by the Cline Center for Advanced Research in the Social Sciences. (8hrs/week at $12.50/hour + possible independent study – 100% REMOTE WORK).
COVID-19 news analytics:
We seek to understand how public health emergencies are reported and to assess the polarization and politicization of the U.S. news coverage. You will be responsible for testing and improving search parameters, investigating contextual information such as media bias and media circulation, using text mining and data science, and close reading of sample texts. You will work closely with a student who has worked on the opioid crisis – see the past work following poster (try the link TWICE – you have to log in with an Illinois NetID):
https://compass2g.illinois.edu/webapps/discussionboard/do/message?action=list_messages&course_id=_50281_1&nav=discussion_board&conf_id=_247818_1&forum_id=_417427_1&message_id=_6264991_1

Applications should be submitted here: https://forms.illinois.edu/sec/742264484

DEADLINE: 5 pm Central Time SUNDAY AUGUST 30, 2020

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#ShutDownSTEM #strike4blacklives #ShutDownAcademia

June 10th, 2020
by jodi

I greatly appreciated receiving messages from senior people about their participation in the June 10th #ShutDownSTEM #strike4blacklives #ShutDownAcademia.

In that spirit, I am sharing my email bounce message for tomorrow, and the message I sent to my research lab.


Email bounce:

I am not available by email today: 
This June 10th is a day of action about understanding and addressing racism, and its impact on the academy, and on STEM. 
-Jodi


Email to my research lab

Wednesday is a day of action about understanding and addressing racism, and its impact on the academy, and on STEM.

I strongly encourage you to use tomorrow for this purpose.

Specifically, I invite you to think about what undoing racism – moving towards antiracism – means, and what you can do. One single day, by itself, will not cure racism; but identifying what we can do on an ongoing basis, and taking those actions day after day – that can and will have an impact.

And, if racism is vivid in your daily life, make #ShutDownSTEM a day of rest.

If tomorrow doesn’t suit, I encourage you to reserve a day over the course of the next week, to replace your everyday duties.

What does taking this time actually mean? It means scheduling a dedicated block of time to learn more; rescheduling meetings; shutting down your email; reading books and articles and watching videos; and taking time to reflect on recent events and the stress that they cause every single person in our community.

What am I doing personally? I’ve cancelled meetings tomorrow, and set an email bounce. I will spend part of the day to think more seriously about what real antiracist action looks like from my position, as a white female academic.

This week I will also be using time to re-read White Fragility, to finish Dreamland Burning (a YA novel about the 1921 Tulsa race riot), and to investigate how to bring bystander training to the iSchool. I will also be thinking about the relationship of racism to other forms of oppression – classism, sexism, homophobia, transphobia, xenophobia.

If you are looking for readings of your own, I can point to a list curated by an Anti-Racism Task Force:
https://idea.illinois.edu/education

For basic information, #ShutDownSTEM #strike4blacklives #ShutDownAcademia website:
https://www.shutdownstem.com
Physicists’ Particles for Justice:
https://www.particlesforjustice.org

-Jodi

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QOTD: Storytelling in protest and politics

March 16th, 2020
by jodi

I recently read Francesca Polletta‘s book It was like a fever: Storytelling in protest and politics (2006, University of Chicago Press).

I recommend it! It will appeal to researchers interested in topics such as narrative, strategic communication, (narrative) argumentation, or epistemology (here, of narrative). Parts may also interest activists.

The book’s case studies are drawn from the Student Nonviolent Coordinating Committee (SNCC) (Chapters 2 & 3); online deliberation about the 9/11 memorial (Listening to the City, summer 2002) (Chapter 4); women’s stories in law (including, powerfully, battered women who had killed their abusers, and the challenges in making their stories understandable) (Chapter 5); references to Martin Luther King by African American Congressmen (in the Congressional Record) and by “leading back political figures who were not serving as elected or appointed officials” (Chapter 6). Several are extended from work Polletta previously published from 1998 through 2005 (see page xiii for citations).

The conclusion—”Conclusion: Folk Wisdom and Scholarly Tales” (pages 166-187)—takes up several topics, starting with canonicity, interpretability, ambivalence. I especially plan to go back to the last two sections: “Scholars Telling Stories” (pages 179-184)—about narrative and storytelling in analysts’ telling of events—and “Towards a Sociology of Discursive Forms” (pages 185-187)—about investigating the beliefs and conventions of narrative and its institutional conventions (and relating those to conventions of other “discursive forms” such as interviews). These set forward a research agenda likely useful to other scholars interested in digging in further. These are foreshadowed a bit in the introduction (“Why Stories Matter”) which, among other things, sets out the goal of developing “A Sociology of Storytelling”.

A few quotes I noted—may give you the flavor of the book:

page 141: “But telling stories also carries risks. People with unfamiliar experiences have found those experiences assimilated to canonical plot lines and misheard as a result. Conventional expectations about how stories work, when they are true, and when they are appropriate have also operated to diminish the impact of otherwise potent political stories. For the abused women whom juries disbelieved because their stories had changed in small details since their first traumatized [p142] call to police, storytelling has not been especially effective. Nor was it effective for the citizen forum participants who did not say what it was like to search fruitlessly for affordable housing because discussions of housing were seen as the wrong place in which to tell stories.”

pages 166-167: “So which is it? Is narrative fundamentally subversive or hegemonic? Both. As a rhetorical form, narrative is equipped to puncture reigning verities and to uphold them. At times, it seems as if most of the stories in circulation are subtly or not so subtly defying authorities; at others as if the most effective storytelling is done by authorities. To make it more complicated, sometimes authorities unintentionally undercut their own authority when they tell stories. And even more paradoxically, undercutting their authority by way of a titillating but politically inconsequential story may actually strengthen it. Dissenters, for their part, may find their stories misread in ways that support the very institutions that are challenging….”For those interested in the relations between storytelling, protest, and politics, this all suggests two analytical tasks. One is to identify the features of narrative that allow it to [p167] achieve certain rhetorical effects. The other is to identify the social conditions in which those rhetorical effects are likely to be politically consequential. The surprise is that scholars of political processes have devoted so little attention to either task.”

pages 177-8 – “So institutional conventions of storytelling influence what people can do strategically with stories. In the previously pages, I have described the narrative conventions that operate in legal adjudication, media reporting, television talk shows, congressional debate, and public deliberation. Sociolinguists have documented such conventions in other settings: in medical intake interviews, for example, parole hearings, and jury deliberations. One could certainly generate a catalogue of the institutional conventions of storytelling. To some extent, those conventions reflect the peculiarities of the institution as it has developed historically. They also serve practical functions; some explicit, others less so. I have argued that the lines institutions draw between suitable and unsuitable occasions for storytelling or for certain kinds of stories serve to legitimate the institution.” [specific examples follow] ….”As these examples suggest, while institutions have different conventions of storytelling, storytelling does some of the same work in many institutions. It does so because of broadly shared assumptions about narrative’s epistemological status. Stories are generally thought to be more affecting by less authoritative than analysis, in part because narrative is associated with women rather than men, the private sphere rather than the public one, and custom rather than law. Of course, conventions of storytelling and the symbolic associations behind them are neither unitary nor fixed. Nor are they likely to be uniformly advantageous for those in power and disadvantageous for those without it. Narrative’s alignment [179] along the oppositions I noted is complex. For example, as I showed in chapter 5, Americans’ skepticism of expert authority gives those telling stories clout. In other words, we may contrast science with folklore (with science seen as much more credible), but we may also contrast it with common sense (with science seen as less credible). Contrary to the lamentation of some media critics and activists, when disadvantaged groups have told personal stories to the press and on television talk shows, they have been able to draw attention not only to their own victimization but to the social forces responsible for it.

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Knowledge Graphs: An Aggregation of Definitions

March 3rd, 2019
by jodi

I am not aware of a consensus definition of knowledge graph. I’ve been discussing this for awhile with Liliana Giusti Serra, and the topic came up again with my fellow organizers of the knowledge graph session at US2TS as we prepare for a panel.

I’ve proposed the following main features:

  • RDF-compatible, has a defined schema (usually an OWL ontology)
  • items are linked internally
  • may be a private enterprise dataset (e.g. not necessarily openly available for external linking) or publicly available
  • covers one or more domains

Below are some quotes.

I’d be curious to hear of other definitions, especially if you think there’s a consensus definition I’m just not aware of.

“A knowledge graph consists of a set of interconnected typed entities and their attributes.”
Jose Manuel Gomez-Perez, Jeff Z. Pan, Guido Vetere and Honghan Wu. “Enterprise Knowledge Graph: An Introduction.”  In Exploiting Linked Data and Knowledge Graphs in Large Organisations. Springer. Part of the whole book: http://link.springer.com/10.1007/978-3-319-45654-6

“A knowledge graph is a structured dataset that is compatible with the RDF data model and has an (OWL) ontology as its schema. A knowledge graph is not necessarily linked to external knowledge graphs; however, entities in the knowledge graph usually have type information, defined in its ontology, which is useful for providing contextual information about such entities. Knowledge graphs are expected to be reliable, of high quality, of high accessibility and providing end user oriented information services.”

Boris Villazon-Terrazas, Nuria Garcia-Santa, Yuan Ren, Alessandro Faraotti, Honghan Wu, Yuting Zhao, Guido Vetere and Jeff Z. Pan .  “Knowledge graphs: Foundations”. In Exploiting Linked Data and Knowledge Graphs in Large Organisations.  Springer. Part of the whole book: http://link.springer.com/10.1007/978-3-319-45654-6


“The term Knowledge Graph was coined by Google in 2012, referring to their use of semantic knowledge in Web Search (“Things, not strings”), and is recently also used to refer to Semantic Web knowledge bases such as DBpedia or YAGO. From a broader perspective, any graph-based representation of some knowledge could be considered a knowledge graph (this would include any kind of RDF dataset, as well as description logic ontologies). However, there is no common definition about what a knowledge graph is and what it is not. Instead of attempting a formal definition of what a knowledge graph is, we restrict ourselves to a minimum set of characteristics of knowledge graphs, which we use to tell knowledge graphs from other collections of knowledge which we would not consider as knowledge graphs. A knowledge graph

  1. mainly describes real world entities and their interrelations, organized in a graph.

  2. defines possible classes and relations of entities in a schema.

  3. allows for potentially interrelating arbitrary entities with each other.

  4. covers various topical domains.”

Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic web8(3), 489-508.

“ISI’s Center on Knowledge Graphs research group combines artificial intelligence, the semantic web, and database integration techniques to solve complex information integration problems. We leverage general research techniques across information-intensive disciplines, including medical informatics, geospatial data integration and the social Web.”

Just as I was “finalizing” my list to send to colleagues, I found a poster all about definitions:
Ehrlinger, L., & Wöß, W. (2016). Towards a Definition of Knowledge Graphs. SEMANTiCS (Posters, Demos, SuCCESS)48http://ceur-ws.org/Vol-1695/paper4.pdf
Its Table 1: Selected definitions of knowledge graph has the following definitions (for citations see that paper)

“A knowledge graph (i) mainly describes real world entities and their interrelations, organized in a graph, (ii) defines possible classes and relations of entities in a schema, (iii) allows for potentially interrelating arbitrary entities with each other and (iv) covers various topical domains.” Paulheim [16]

“Knowledge graphs are large networks of entities, their semantic types, properties, and relationships between entities.” Journal of Web Semantics [12]

“Knowledge graphs could be envisaged as a network of all kind things which are relevant to a specific domain or to an organization. They are not limited to abstract concepts and relations but can also contain instances of things like documents and datasets.” Semantic Web Company [3]

“We define a Knowledge Graph as an RDF graph. An RDF graph consists of a set of RDF triples where each RDF triple (s, p, o) is an ordered set of the following RDF terms: a subjects∈U∪B,apredicatep∈U,andanobjectU∪B∪L. AnRDFtermiseithera URI u ∈ U, a blank node b ∈ B, or a literal l ∈ L.” Färber et al. [7]

“[…] systems exist, […], which use a variety of techniques to extract new knowledge, in the form of facts, from the web. These facts are interrelated, and hence, recently this extracted knowledge has been referred to as a knowledge graph.” Pujara et al. [17]


“A knowledge graph is a graph that models semantic knowledge, where each node is a real-world concept, and each edge represents a relationship between two concepts”

Fang, Y., Kuan, K., Lin, J., Tan, C., & Chandrasekhar, V. (2017). Object detection meets knowledge graphs.
https://oar.a-star.edu.sg/jspui/handle/123456789/2147


“things not strings” – Google

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QOTD: Doing more requires thinking less

December 1st, 2018
by jodi

by the aid of symbolism, we can make transitions in reasoning almost mechanically by the eye which would otherwise call into play the higher faculties of the brain.

…Civilization advances by extending the number of important operations that we can perform without thinking about them. Operations of thought are like cavalry charges in a battle — they are strictly limited in number, they require fresh horses, and must only be made at decisive moments.

One very important property for symbolism to possess is that it should be concise, so as to be visible at one glance of the eye and be rapidly written.

– Whitehead, A.N. (1911). An introduction to mathematics, Chapter 5, “The Symbolism of Mathematics” (page 61 in this version)
HT to Santiago Nuñez-Corrales (Illinois page for Santiago Nuñez-Corrales, LinkedIn for Santiago Núñez-Corrales) who used part of this quote in a Conceptual Foundations Group talk, Nov 29.

From my point of view, this is why memorizing multiplication tables is not now irrelevant; why new words for concepts are important; and underlies a lot of scientific advancement.

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QOTD: Sally Jackson on how disagreement makes arguments more explicit

June 19th, 2018
by jodi

Sally Jackson explicates the notion of the “disagreement space” in a new Topoi article:

“a position that remains in doubt remains in need of defense” ((p 12, Sally Jackson. Reason-Giving and the Natural Normativity of Argumentation. Topoi. 2018 Online First. http://doi.org/10.1007/s11245-018-9553-5))

 

“The most important theoretical consequence of seeing argumentation as a system for management of disagreement is a reversal of perspective on what arguments accomplish. Are arguments the means by which conclusions are built up from established premises? Or are they the means by which participants drill down from disagreements to locate how it is that they and others have arrived at incompatible positions? A view of argumentation as a process of drilling down from disagreements suggests that arguers themselves do not simply point to the reasons they hold for a particular standpoint, but sometimes discover where their own beliefs come from, under questioning by others who do not share their beliefs. A logical analysis of another’s argument nearly always involves first making the argument more explicit, attributing more to the author than was actually said. This is a familiar enough problem for analysts; my point is that it is also a pervasive problem for participants, who may feel intuitively that something is seriously wrong in what someone else has said but need a way to pinpoint exactly what. Getting beliefs externalized is not a precondition for argument, but one of its possible outcomes.” ((p 10, Sally Jackson. Reason-Giving and the Natural Normativity of Argumentation. Topoi. 2018 Online First. http://doi.org/10.1007/s11245-018-9553-5))

From Sally Jackson’s Reason-Giving and the Natural Normativity of Argumentation. ((Sally Jackson. Reason-Giving and the Natural Normativity of Argumentation. Topoi. 2018 Online First. http://doi.org/10.1007/s11245-018-9553-5))

The original treatment of disagreement space is cited to a book chapter revising an ISSA 1992 paper ((Jackson S (1992) “Virtual standpoints” and the pragmatics of conversational argument. In: van Eemeren FH, Grootendorst R, Blair JA, Willard CA (eds) Argument illuminated. International Centre for the Study of Argumentation, Amsterdam, pp. 260–226)), somewhat harder to get one’s hands on.

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QOTD: Working out scientific insights on paper, Lavoisier case study

July 12th, 2017
by jodi

…language does do much of our thinking for us, even in the sciences, and rather than being an unfortunate contamination, its influence has been productive historically, helping individual thinkers generate concepts and theories that can then be put to the test. The case made here for the constitutive power of figures [of speech] per se supports the general point made by F.L. Holmes in a lecture addressed to the History of Science Society in 1987. A distinguished historian of medicine and chemistry, Holmes based his study of Antoine Lavoisier on the French chemist’s laboratory notebooks. He later examined drafts of Lavoisier’s published papers and discovered that Lavoisier wrote many versions of his papers and in the course of careful revisions gradually worked out the positions he eventually made public (Holmes, 221). Holmes, whose goal as a historian is to reconstruct the careful pathways and fine structure of scientific insights, concluded from his study of Lavoisier’s drafts

We cannot always tell whether a thought that led him to modify a passage, recast an argument, or develop an alternative interpretation occurred while he was still engaged in writing what he subsequently altered, or immediately afterward, or after some interval during which he occupied himself with something else; but the timing is, I believe, less significant than the fact that the new developments were consequences of the effort to express ideas and marshall supporting information on paper (225).

– page xi of Rhetorical Figures in Science by Jeanne Fahnestock, Oxford University Press, 1999.

She is quoting Frederich L. Holmes. 1987. Scientific writing and scientific discovery. Isis 78:220-235. DOI:10.1086/354391

As Moore summarizes,

Lavoisier wrote at least six drafts of the paper over a period of at least six months. However, his theory of respiration did not appear until the fifth draft. Clearly, Lavoisier’s writing helped him refine and understand his ideas.

Moore, Randy. Language—A Force that Shapes Science. Journal of College Science Teaching 28.6 (1999): 366. http://www.jstor.org/stable/42990615
(which I quoted in
a review I wrote recently)

Fahnestock adds:
“…Holmes’s general point [is that] there are subtle interactions ‘between writing, thought, and operations in creative scientific activity’ (226).”

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David Liebovitz: Achieving Care transformation by Infusing Electronic Health Records with Wisdom

May 1st, 2017
by jodi

Today I am at the Health Data Analytics summit. The title of the keynote talk is Achieving Care transformation by Infusing Electronic Health Records with Wisdom. It’s a delight to hear from a medical informaticist: David M. Liebovitz (publications in Google Scholar), MD, FACP, Chief Medical Information Officer, The University of Chicago. He graduated from University of Illinois in electrical engineering, making this a timely talk as the engineering-focused Carle Illinois College of Medicine gets going.

David Liebovitz started with a discussion of the data problems — problem lists, medication lists, family history, rules, results, notes — which will be familiar to anyone using EHRs or working with EHR data. He draws attention also to the human problems — both in terms of provider “readiness” (e.g. their vision for population-level health) as well as about “current expectations”. (An example of such an expectation is a “main clinician satisfier” he closed with: U Chicago is about to turn on outbound faxing from the EHR!) He mentioned also the importance of resilience.

He mentioned customizing systems as a risk when the vendor makes upstream changes (this is not unique to healthcare but a threat to innovation and experimentation with information systems in other industries.) Still, in managing the EHR, there is continual optimization, scored based on a number of factors. He mentioned:

  • Safety
  • Quality/patient experience
  • Regulatory/legal
  • Financial
  • Usability/productivity
  • Availability of alternative solutions

As well as weighting for old requests.

He emphasized the complexity of healthcare in several ways:

complexity of drug purchasing

An image from “Prices That Are Too High”, Chapter 5, The Healthcare Imperative: Lowering Costs and Improving Outcomes: Workshop Series Summary (2010)

  • Icosystem’s diagram of the complexity of the healthcare system

Complexity of the healthcare system

Icosystem – complexity of the healthcare system

  • Another complexity is the modest impact of medical care compared to other factors
    • such as the impact of socioeconomic and political context on equity in health and well-being (see the WHO image below).
    • For instance, there is a large impact of health behaviors, which “happen in larger social contexts.” (See the Relative Contribution of Multiple Determinants to Health, August 21, 2014, Health Policy Briefs)

Given this complexity, David Liebovitz stresses that we need to start with the right model, “simultaneously improving population health, improving the patient experience of care, and reducing per capita cost”. (See Stiefel M, Nolan K. A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and Per Capita Cost. IHI Innovation Series white paper. Cambridge, Massachusetts: Institute for Healthcare Improvement; 2012).

triple aims to measure healthcare improvement

Table 1 from Stiefel M, Nolan K. A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and Per Capita Cost. IHI Innovation Series white paper. Cambridge, Massachusetts: Institute for Healthcare Improvement; 2012.

Given the modest impact of medical care, and of data, he suggests that we should choose the right outcomes.

David Liebovitz says that “not enough attention has been paid to usability”; I completely agree and suggest that information scientists, human factors engineeers, and cognitive ergonomists help mainstream medical informaticists fill this gap. He put up Jakob Nielsen’s 10 usability heuristics for user interface design A vivid example is whether a patient’s resuscitation preferences are shown (which seems to depend on the particular EHR screen): the system doesn’t highlight where we are in the system. For providers, he says user control and freedom are very important. He suggests that there are only a few key tasks. A provider should be able to do ANY of these things wherever they are in the chart:

  • put a note
  • order something
  • send a message

Similarly, EHR should support recognition (“how do I admit a patient again?”) rather than requiring recall.

Meanwhile, on the decision support side he highlights the (well-known) problems around interruptions by saying that speed is everything and changing direction is much easier than stopping. Here he draws on some of his own work, describing what he calls a “diagnostic process aware workflow”

David Liebovitz. Next steps for electronic health records to improve the diagnostic process. Diagnosis 2015 2(2) 111-116. doi:10.1515/dx-2014-0070

Can we predict X better? Yes, he says (for instance pointing to Table 3 of “Can machine-learning improve cardiovascular risk prediction using routine clinical data?” and its machine learning analysis of over 300,000 patients, based on variables chosen from previous guidelines and expert-informed selection–generating further support for aspects such as aloneness, access to resources, socio-economic status). But what’s really needed, he says, is to:

  • Predict the best next medical step, iteratively
  • Predict the best next lifestyle step, iteratively
  • (And what to do about genes and epigenetic measures?)

He shows an image of “All of our planes in the air” from flightaware, drawing the analogy that we want to work on “optimal patient trajectories” — predicting what are the “turbulent events” to avoid”. This is not without challenges. He points to three:

He closes suggesting that we:

  • Finish the basics
  • Address key slices of the spectrum
  • Descriptive/prescriptive
  • Begin the prescriptive journey: impact one trajectory at a time.

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QOTD: Scholarly communication online, circa 1996

December 2nd, 2015
by jodi

Here is a glimpse into scholarly communication 20 years ago, from a paper about Alzforum, the Alzheimer Research Forum website. “In July of 1996, the website made its debut at the International Conference on Alzheimer’s Disease and Related Disorders in Osaka, Japan.” ((page 458, Kinoshita, June, and Gabrielle Strobel. “Alzheimer Research Forum: a knowledge base and e-community for AD research.” in Alzheimer: 100 Years and Beyond, Mathias Jucker, Konrad Beyreuther, Christian Haass, Roger M. Nitsch, Yves Christen, eds. Berlin Heidelberg:Springer-Verlag, 2006: 457-463.))

Having established a foothold in cyberspace, the challenge for Alzforum was and continues to be to define new types of scientific publishing that take advantage of the speed and wide distribution of the Web and to curate and add value to information available from other public sources. This is a perennial challenge, thanks to the rapid advances in biomedical resources on the Web.

This uphill struggle, however, seems less strenuous when we compare the current situation with the “old days.” Recall that in 1996, PubMed did not exist! (PubMed was launched in June of 1997.) Medical institutions had access to Medline, but in order for Alzforum to produce its Papers of the Week listings, the editor had to ask the Countway Medical Library at Harvard Medical School to provide weekly text files listing newly indexed AD papers. The Alzforum hired a curator to paraphrase each abstract so that this information could be posted without violating journal copyrights. These documents were manually edited, sent out in a weekly email to the advisors for comments, and compiled into a static HTML page. Looking back, we can see that the entire process seems as antiquated as the hand-copying of manuscripts in the Middle Ages.

(emphasis mine)

From pages 459-460 of “Alzheimer Research Forum: a knowledge base and e-community for AD research” ((Kinoshita, June, and Gabrielle Strobel. “Alzheimer Research Forum: a knowledge base and e-community for AD research.” in Alzheimer: 100 Years and Beyond, Mathias Jucker, Konrad Beyreuther, Christian Haass, Roger M. Nitsch, Yves Christen, eds. Berlin Heidelberg:Springer-Verlag, 2006: 457-463.))

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