MétaCan
Menu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEmergency Medicine News · 2020
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsMcGill University Health CentreMontreal General Hospital
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

FigureFigureThe daily struggle with efficiently managing information is not new for physicians. It is a well-accepted observation that excessive information (novel or established) can become a distraction and even negatively affect physician psychological well-being and consequently clinical efficiency and performance. (J Biosci Med. 2015;3[11]:124.) An important insight from behavioral theory is that careful consideration in information analysis and subsequent decision-making is mentally taxing, leading people to shy away from engaging in cognitively demanding reasoning when tired, which can clearly affect clinical work. Information overload can influence physicians to fall inappropriately into cognitive biases leading to clinical error, most commonly availability bias (believing that ideas that come readily to mind are more representative than is the case) and confirmation bias (focusing on information that reinforces preconceived notions at the expense of contradictory information). Information overload occurs when a person encounters a significant amount of seemingly relevant information needing to be processed, resulting in difficulty in distinguishing which data are reliable and helpful. This process can culminate in feelings of frustration and cause anxiety, fatigue, decision fatigue, and paralysis of action. Behavioral psychology studies in the 1970s revealed an inverted U-shaped relationship between information load and decision quality, which increases as information load increases and then it begins to decrease, especially under time pressure. Complex Definition Defining information overload, however, is much more complex than just being overburdened with a large amount of information. Many other terms have been used, such as information flood, information smog, information explosion, information glut, knowledge overload, information fatigue syndrome, data overload, and data explosion, but there is no universal definition. (Int J Res Engineer Tech. 2014;3[17]:27.) Health care providers, particularly physicians, can relate to feelings of exasperation from information overload, especially now during the COVID-19 era. Experience from the 2003 severe acute respiratory syndrome (SARS) outbreak and early reports of COVID-19 show that they experience considerable psychological distress for myriad reasons—providing direct patient care, vicarious psychological trauma, quarantine, and self-isolation. Physicians disproportionately bear the additional physical and psychological burdens associated with pandemics. (JAMA Netw Open. 2020;3[3]:e203976; JAMA Netw Open. 2020;3[6]:e2010705.) This distress is likely potentiated by the frequent and significant amount of information and misinformation on the internet and social media. Email Barometer My daily email load during the pandemic underscores the scope of the phenomenon. This is purely anecdotal and may under- or overestimate the scope of the information overload in relation to the average practicing physician. It is possible that my conclusions underestimate the scope of the information overload because only emails were analyzed, no social media were used, and the emails were not equal in their complexity. Overestimation may have occurred because of my specialty, practice in a university academic hospital, and leadership position in education. Before COVID-19, I received an average of 20 emails a day. For the six weeks after the pandemic started, from March 25 to May 6, I received an extra 15 emails related to COVID-19 every day. Fifty percent of those emails were departmental, 30 percent were institutional, 15 percent were academic and related to research and teaching, and five percent were personal. Seventy percent of the emails had similar or repetitive content, and 15 percent had contradictory content. Thirty-five percent of that content was related to logistics, 30 percent was general information, 25 percent was about diagnostic and therapeutic processes, and 10 percent was related to education and questionnaires (five percent each). Ninety-five percent of the emails were complicated with multiple concepts or ideas, and five percent were simple. Half of the emails required action in less than 48 hours, and I spent an hour each day reading them. An analysis of the raw data highlights the burden of information overload and how it clearly adds to an already full intrinsic cognitive load—the cognitive weight of the information determined by the complexity of the material being processed. A few striking trends with potential detrimental consequences to cognitive load and time management were obvious: the 75 percent increase in the volume of emails, the significant overlap of similar and repetitive information, and the significant proportion of the information that contained at least one contradictory statement. Quality and Conclusions Much of the COVID-19 medical literature pushed was of poor quality and made few tangible conclusions and links between evidence and recommendations, which was not dissimilar to the findings of at least one study during this time. (BMJ. 2020;369:m1936.) The overwhelming majority of the information was complex. Having so much information for decision-making can result in the frustrating paradox that there is actually a lack of information within that flood of information. Many industries other than health care are trying to mitigate the effects of information overload (accounting, organization science, marketing and management information systems), and we should emulate these efforts. Human-computer interaction and artificial intelligence hold promise. Using the concepts of data display and information visualization, data can be transformed into a visual form to make use of humans' natural visual capabilities. (Hospitalist. 2006;2006[3]; https://bit.ly/306HvjT.) Machine learning natural language processing might help pull relevant information and provide alerts to action from sources to physicians. Another major source of cognitive load is sifting through myriad sources of information, many with similar and contradictory content. Prioritizing, delegating, and significantly limiting outsourcing privilege would ease the information overload burden. (Otolaryngol Head Neck Surg. 2020;163[1]:60.) Self-reflection on many levels may also help. Figure out your learning style; there are many ways to access information. Knowing how one likes to consume information is vital (reading, listening, visually) and will help with retention. Prioritize your sources by seeking out curated content from limited places, and optimize the timing of information processing. Not only is the time of day important when processing information (decision fatigue is more likely later in the day) but so is individualizing the quantity of information processed in one sitting. The medical information explosion is here to stay and can overwhelm physicians. We need to explore and identify individual processing of information and use technology developed in different industries to manage information overload.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.547
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0260.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.331
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it