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Record W2022215392 · doi:10.3163/1536-5050.102.3.013

Evidence-based practice instruction by faculty members and librarians in North American optometry and ophthalmology programs

2014· article· en· W2022215392 on OpenAlexafffund
K. Macdonald, Patricia K. Hrynchak, Marlee M. Spafford

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsMedical educationOphthalmologyEvidence-based practiceMedicineOptometryAlternative medicine

Abstract

fetched live from OpenAlex

North American optometry and ophthalmology faculty members and vision science librarians were surveyed online (14% response rate) about teaching evidence-based practice (EBP). Similar to studies of other health care programs, all five EBP steps (Ask, Acquire, Appraise, Apply, Assess) were taught to varying degrees. Optometry and ophthalmology EBP educators may want to place further emphasis on (1) the Apply and Assess steps, (2) faculty- and student-generated questions and self-assessment in clinical settings, (3) online teaching strategies, (4) programmatic integration of EBP learning objectives, and (5) collaboration between faculty members and librarians.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.090
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.065
GPT teacher head0.458
Teacher spread0.393 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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

Citations13
Published2014
Admission routes2
Has abstractyes

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