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Record W2460400726 · doi:10.1097/acm.0000000000000723

Towards Robust Validity Evidence for Learning Environment Assessment Tools

2015· letter· en· W2460400726 on OpenAlexaboutno aff
Lawrence K. Loo, John M. Byrne

Bibliographic record

VenueAcademic Medicine · 2015
Typeletter
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPredictive validityInternal validityExternal validityInternal consistencyReliability (semiconductor)Medical educationApplied psychologyClinical psychologySocial psychologyPsychometricsMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

To the Editor: Colbert-Getz and colleagues’1 review of learning environment (LE) assessment tools is both timely and relevant. The authors use four of the five categories of validity evidence from the American Psychological and Educational Research Associations to arrive at a total validity evidence score for each tool they reviewed to judge the quality of both medical student and resident LE perceptions. We wonder, however, if the authors used only the initial publications to arrive at their total validity evidence scores. For example, while the Medical Student Learning Environment Survey’s (MSLES) original publication received a total score of 3/8 (38%), subsequent publications from Australia and Canada examined the Internal Structure and Relationship to Other Variables criteria.2,3 Both studies used factor analysis to independently confirm that the individual scales of the MSLES represent one dominant factor. Clarke et al2 also examined the retest reliability and internal consistency, while Rusticus et al3 correlated the MSLES to student satisfaction and academic performance. Applying the authors’ validity criteria, we would have given an additional rating of 2 (“strong” evidence) for Internal Structure and a score of 1 (“weak” evidence) for Relationship to Other Variables. The total validity evidence score of the MSLES would therefore increase to 6/8 (75%). Similarly, for measuring the resident LE, the authors give the VA Learners’ Perception Survey (LPS) a total validity score of 2/8 (25%). The original publication by Keitz et al4 used focus groups of medical students and residents in the initial development of the LPS, and factor analysis was used to collapse the original 57 questions into four major domains. Internal consistency using a mixed-effects model was further verified in a subsequent publication by Cannon et al.5 We would have given an additional rating of 1 for Response Process and 2 for Internal Structure, increasing the total validity evidence score to 5/8 (63%). Both of these updated scores for the MSLES and LPS would be the highest scores listed for validity evidence in undergraduate and graduate medical education, respectively. We also wonder if the authors assessed the interrater reliability used to assess the validity evidence, since their checklist was adapted from Beckman et al,6 which found kappa values ranging from −0.10 to 0.96 and was particularly poor for rating the Response Process criteria. Lawrence K. Loo, MD Vice chair, Education and Faculty Development, Department of Medicine, and professor of medicine, Loma Linda University School of Medicine, Loma Linda, California; [email protected] John M. Byrne, DO Associate chief of staff, Education, VA Loma Linda Healthcare System, and associate professor of medicine, Loma Linda University School of Medicine, Loma Linda, California.

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)0.389
metaresearch head score (Gemma)0.850
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.611
Threshold uncertainty score0.753

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3890.850
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0110.007
Science and technology studies0.0030.012
Scholarly communication0.0170.013
Open science0.0100.012
Research integrity0.0250.027
Insufficient payload (model declined to judge)0.0060.003

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 head0.713
GPT teacher head0.557
Teacher spread0.156 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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".

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Citations2
Published2015
Admission routes1
Has abstractyes

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