MétaCan
Menu
Back to cohort
Record W1638400270 · doi:10.3138/ptc.2015-10e

Diminishing Effect Sizes with Repeated Exposure to Evidence-Based Practice Training in Entry-Level Health Professional Students: A Longitudinal Study

2015· article· en· W1638400270 on OpenAlexvenueno aff
Lucy K. Lewis, Sze C. Wong, Louise Wiles, Maureen McEvoy

Bibliographic record

VenuePhysiotherapy Canada · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsEntry LevelTraining (meteorology)Medical educationLongitudinal studyPhysical therapyPhysical medicine and rehabilitationPsychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the magnitude of change in outcomes after repeated exposure to evidence-based practice (EBP) training in entry-level health professional students. METHOD: Using an observational cross-sectional analytic design, the study tracked 78 students in physiotherapy, podiatry, health science, medical radiations, and human movement before and after two sequential EBP courses. The first EBP course was aimed at developing foundational knowledge of and skills in the five steps of EBP; the second was designed to teach students to apply these steps. Two EBP instruments were used to collect objective (actual knowledge) and self-reported (terminology, confidence, practice, relevance, sympathy) data. Participants completed both instruments before and after each course. RESULTS: Effect sizes were larger after the first course than after the second for relevance (0.72 and 0.26, respectively), practice (1.23 and 0.43), terminology (2.73 and 0.84), and actual knowledge (1.92 and 1.45); effect sizes were larger after the second course for sympathy (0.03 and 0.14) and confidence (0.81 and 1.12). CONCLUSIONS: Knowledge and relevance changed most meaningfully (i.e., showed the largest effect size) for participants with minimal prior exposure to training. Changes in participants' confidence and attitudes may require a longer time frame and repeated training exposure.

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)0.007
metaresearch head score (Gemma)0.002
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 score0.077
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.248
GPT teacher head0.542
Teacher spread0.294 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePhysiotherapy CanadaSame topicHealth Sciences Research and EducationFrench-language works237,207