How Consistent Is Competent? Examining Variance in Psychomotor Skills Assessment
Bibliographic record
Abstract
PURPOSE: Direct assessment of trainee performance across time is a core tenet of competency-based medical education. Unlike variability of psychomotor skills across levels of expertise, performance variability exhibited by a particular trainee across time remains unexplored. The goal of this study was to document the consistency of individual surgeons' technical skill performance. METHOD: A secondary analysis of assessment data (collected in 2010-2012, originally published in 2015) generated by a prospective cohort of participants at Montreal Children's Hospital with differing levels of expertise was conducted in 2017. Trained raters scored blinded recordings of a myringotomy and tube insertion performed 4 times by junior and senior residents and attending surgeons over a 6-month period using a previously reported assessment tool. Descriptive exploratory analyses and univariate comparison of standard deviations (SDs) were conducted to document variability within individuals across time and across training levels. RESULTS: Thirty-six assessments from 9 participants were analyzed. The SD of scores for junior residents was highly variable (5.8 out of a scale of 30 compared with 1.8 for both senior residents and attendings [F(2,19) = 5.68, P < 0.05]). For a given individual, the range of scores was twice as large for junior residents than for senior residents and attendings. CONCLUSIONS: Surgical residents may display highly variable performances across time, and individual variability appears to decrease with increasing expertise. Operative skill variability could be underrepresented in direct observation assessment; emphasis on an adequate amount of repetitive evaluations for junior residents may be needed to support judgments of competence or entrustment.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".