Applying Generalizability Theory to High-Stakes Objective Structured Clinical Examinations in a Naturalistic Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: The purpose of this project was to determine if generalizability theory could be successfully applied to a high-stakes licensure objective structured clinical examination as part of its normal administrative procedures and whether the analysis could yield useful information with regard to sources of variance. METHODS: The anonymized data received from the Canadian Chiropractic Examining Board for its June 2005 Clinical Skills Examination were analyzed with generalizability theory. Variance components were estimated with SPSS 11.5 (SPSS Inc, Chicago, Ill) as partially nested data. The data included 182 candidates, 43 raters, 40 standardized patient actors, and 18 individual cases. RESULTS: Internal consistency estimates (Cronbach alpha) were .86 for day 1 and .91 for day 2. The alpha estimates for stations averaged .68 for day 1 and .74 for day 2. The generalizability-coefficient for the day 1 exam was .65 and for the day 2 was .42. G-coefficients for stations averaged .63 for day 1 and .74 for day 2. On day 1, the raters contributed 7% of the variance, and on day 2, the raters contributed 8%. CONCLUSIONS: Generalizability theory can contribute to the understanding of sources of variance and provide direction for the improvement of individual stations. The size of the rater variance in a station may also indicate the need for increased training in that station or the need to make the scoring checklist more clear and definitive. Generalizability theory, however, must be cautiously applied, and it requires careful selection of the floating raters and vigorous training of the raters in each station.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it