Not in the file: How competency committees work with undocumented contributions
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Competence committees (CCs) centre their work around documentation of trainees' performance; undocumented contributions (i.e. informal, unrecorded material like personal judgements, experiential anecdotes and contextual information) evoke suspicion even though they may play a role in decision making. This qualitative multiple case study incorporates insights from a social practice perspective on writing to examine the use of undocumented contributions by the CCs of two large post-graduate training programmes, one in a more procedural (MP) speciality and the other in a less procedural (LP) one. METHODS: Data were collected via observations of meetings and semi-structured interviews with CC members. In the analysis, conversations were organised into triptychs of lead-up, undocumented contribution(s), and follow-up. We then created thick descriptions around the undocumented contributions, drawing on conversational context and interview data to assign possible motivations and significance. RESULTS: We found no instances in which undocumented contributions superseded the contents of a trainee's file or stood in for missing documentation. The number of undocumented contributions varied between the MP CC (six instances over two meetings) and the LP CC (22 instances over three meetings). MP CC discussions emphasised Entrustable Professional Activity (EPA) observations, whereas LP CC members paid more attention to narrative data. The divergent orientations of the CCs-adding an 'advis[ing]/guid[ing]' role versus focusing simply on evaluation-offers the most compelling explanation. In lead-ups, undocumented contributions were prompted by missing and flawed documentation, conflicting evidence and documentation at odds with members' perceptions. Recognising other 'red flags' in documentation often required professional experience. In follow-ups, purposes served by undocumented contributions varied with context and were difficult to generalise; we, therefore, provide deeper analysis of two vignettes to illustrate. CONCLUSIONS: Our data suggest undocumented contributions often serve best efforts to ground decisions in documentation. We would encourage CC practices and policies be rooted in more nuanced approaches to documentation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it