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 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 | 0.056 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".