Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Family Medicine trainees are often assessed in Objective Structured Clinical Examinations (OSCEs). The purpose of this survey is to document the quality in terms of psychometrics and standard setting of OSCEs as used in Family Practice (FP)/General Practice (GP) training programs. METHODS: Nine electronic data bases were searched from inception to December 2015 and included articles were searched in the PubMed single citation matcher. Two authors independently assessed all titles/abstracts/full texts and abstracted data. Articles were searched for OSCEs used for performance assessment of FP/GP trainees. RESULTS: Twenty-one studies were identified which met our criteria published between 1987 and 2014. Content validity was reported in 18, construct validity in nine, and criterion (concurrent and/or predictive) validity in five. Five articles considered the consequences of testing. Internal reliability was reported by 12 studies, inter-rater reliability by seven, generalisability by four. Nine set pass-fail standards of which four were by criterion standards. In addition, we tabulated sources of validity and reliability as with particular reference to medical education. CONCLUSIONS: We found few articles which vigorously provided evidence of validity and reliability. Standard-setting, when done, was normative in all high stakes exams. OSCEs used for formative purposes had lower psychometric standards.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".