Understanding the near-peer relationship: resident perspectives around a novel on-call workplace-based assessment
Bibliographic record
Abstract
Background: Workplace-based assessment (WBA) is a critical component of competency-based medical education (CBME), though literature on WBA for overnight call is limited. We evaluated a WBA tool completed by supervising subspecialty trainees on paediatric residents during subspecialty overnight call, for usefulness facilitating feedback/coaching in this setting. Methods: Web-based surveys were sent to residents pre- and post-WBA tool implementation monthly for four months (August-December 2018), exploring feedback frequency, Likert-scaled opinions of tool feasibility/usefulness facilitating feedback, and qualitative experiences. Assessor comments were categorized as actionable/non-actionable. Quantitative data was summarized using descriptive statistics. Qualitative data was coded to identify themes. Results: = 16 responses), a non-sustained trend of increased Medical Expert feedback was observed. Residents were generally divided or disagreed on tool usefulness facilitating feedback and feasibility. Comments contained actionable feedback in < 10% of completed WBAs. Qualitative analysis revealed barriers to tool-facilitated coaching including: feedback quality and setting/environment, role of senior near-peer as assessor, interpersonal burden in encounters, and tool-specific issues. Conclusions: Increasing frequency of WBA tool completion is not sufficient to achieve CBME goals. Factors impacting feedback/coaching within the resident/near-peer dyad must be addressed.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".