Examining Accuracy of Self-Assessment of In-Training Examination Performance in a Context of Guided Self-Assessment.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In our family medicine residency program, we have established a culture of guided self-assessment through a systematic approach of direct observation of residents and documentation of formative feedback. We have observed that our residents have become more accurate in self-assessing their clinical performance. The objective of this study was to examine whether this improved accuracy extended to residents' self-assessment of their medical knowledge and clinical reasoning on the In-Training Examination (ITE). METHODS: In November each year, residents in their first (PGY1) and second (PGY2) years of residency take the ITE (240 multiple-choice questions). Immediately before and right after taking the ITE, residents complete a questionnaire, self-assessing their knowledge and predicting their performances, overall and in eight high-level domains. Consented data from residents who took the ITE in 2009-2015 (n=380, 60% participation rate) were used in the Generalized Estimating Equations analyses. RESULTS: PGY2 residents outperformed PGY1 residents; Canadian medical graduates consistently outperformed international medical graduates; urban and rural residents performed similarly overall. Residents' pre-post self-assessments were in line with residents' actual performance on the overall examination and in the domains of Adult Medicine and Care of Surgical Patients. The underperforming residents in this study accurately predicted both pre- and post-ITE that they would perform poorly. CONCLUSION: Our findings suggest that the ITE operates well in our program. There was a tendency among residents in this study to appropriately adjust their self-assessment of their overall performance after completing the ITE. Irrespective of the residency year, resident self-assessment was less accurate on individual domains.
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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 | 0.013 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".