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Record W3125720943

Gender Differences in How Physicians Access and Process Information

2019· article· en· W3125720943 on OpenAlexaffabout
Raphael Gotlieb, Jeremie Abitbol, Jeffrey How, I. Ben-Brith, Haim A. Abenhaim, Stuart Lau, Mark Basik, Zeev Rosberger, Nehemia Geva, Walter H. Gotlieb, Alex Mintz

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsHeuristicsTRACE (psycholinguistics)Process (computing)Medical informationContinuing medical educationDecision aidsPsychologyMedical educationMedicineFamily medicineComputer scienceAlternative medicineContinuing education
DOInot available

Abstract

fetched live from OpenAlex

There is an absence of information on how physicians make surgical decisions, and on the effect of gender on the processing of information. A novel web based decision-matrix software was designed to trace experimentally the process of decision making in medical situations. The scenarios included a crisis and non-crisis simulation for endometrial cancer surgery. Gynecologic oncologists, fellows, and residents (42 male and 42 female) in Canada participated in this experiment. Overall, male physicians used more heuristics, whereas female physicians were more comprehensive in accessing clinical information (p < 0.03), utilized alternative-based acquisition processes in the non-crisis scenario (p=0.01), were less likely to consider procedure-related costs (p=0.04), and overall allocated more time to evaluate the information (p < 0.01). Further experiments leading to a better understanding of the cognitive processes involved in medical decision making could influence education and training and impact on patient outcome.

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.

How this classification was reachedexpand

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.286
Teacher spread0.273 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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