Evaluation of a Structured Application Assessment Instrument for Assessing Applications to Canadian Postgraduate Training Programs in Emergency Medicine
Bibliographic record
Abstract
OBJECTIVE: To determine the interrater reliability and predictive validity of a structured instrument for assessing applications submitted to a Fellow of the Royal College of Physicians of Canada (FRCP) emergency medicine residency program. METHODS: An application assessment instrument was derived based on faculty and resident input, institutional and national documents, and previous protocols. The instrument provided a score based on objective anchors for each of four application components. Three assessors were introduced to the instrument in a detailed tutorial session. Assessors were given five applications to score and results were compared for understanding of the scoring principles. The instrument was used in a developmental pilot to assess the 2001 cohort of applications and revised again. Applications for the 2002 study cohort were submitted through a central application service. Assessors used the instrument to score each application independently. Interrater reliability was determined by calculating a two-way mixed-effect Cronbach's alpha. RESULTS: Forty applications were received for the year 2002. Thirty-eight application packages were complete and data collection was complete for all 38. The single-rater reliabilities for the curriculum vitae, personal letter, transcript, reference letters, and overall package were 0.73, 0.52, 0.64, 0.61, and 0.72, respectively. The three-rater reliabilities for the components were 0.89, 0.77, 0.84, and 0.82, respectively. The three-rater reliability of the overall application score was 0.89. CONCLUSIONS: Three-rater reliabilities for each component and the entire application package were high. Multiple assessors are required to generate acceptable reliabilities. Using strict design and implementation principles can lead to a reliable instrument for assessing complex application packages.
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How this classification was reachedexpand
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".