Does general experience affect self‐assessment?
Bibliographic record
Abstract
BACKGROUND: The assessment of clinical competence is critical in medical education. Understanding the effect of general experience on a physician's self-assessment would help design more effective curricula and evaluations of procedural skills in postgraduate training and continuing professional development (CPD). In this observational study, we assessed the effect of general experience on the correlation between confidence and competence amongst experienced clinicians (ECs) and postgraduate trainees (PGTs) when learning an office-based procedure in pessary care. METHODS: We recruited 19 first-year family medicine residents and 18 family medicine faculty members in two outpatient academic clinics. All participants attended a simulation-based workshop for a routine gynaecological office procedure. We used a confidence survey as a measure of the participants' self-assessed competence and an objective structured clinical examination (OSCE) to evaluate participants' competence before and after the workshop. The assessment of clinical competence is critical in medical education RESULTS: We found no significant correlation between confidence and competence at baseline for either group (EC, r = 0.25, p = 0.35; PGT, r = 0.15, p = 0.60). After the workshop, we observed a statistically significant correlation between confidence and competence for ECs (r = 0.60, p = 0.01), but not for PGTs. The change in this correlation was not statistically significant for either group, however. DISCUSSION: Our findings suggest that ECs are not any more accurate in the assessment of their competence compared with PGTs. All procedural skills curricula can benefit from OSCE-format evaluation to better evaluate the improvement in performance of participants.
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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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".