Commentary: audit of case-load and case mix of higher specialist trainees in child and adolescent psychiatry
Bibliographic record
Abstract
The Child and Adolescent Psychiatry Specialist Advisory Sub-Committee (CAPSAC) of the Royal College of Psychiatrists has produced a detailed set of advisory papers covering all aspects of training in child and adolescent psychiatry, the existence of which makes the audit of training a more straightforward task than in the past (Royal College of Psychiatrists Higher Specialist Training Committee, 1999). The paper by Sharp and Morris (see pp. 212–215, this issue) is part of a continuing tradition of audit and evaluation of higher training in child and adolescent psychiatry (Garralda et al, 1983; Bools & Cottrell, 1990; Smart & Cottrell, 2000). In the past, supervision (or lack of it) has been a preoccupation (see Kingsbury & Allsopp, 1994). However, the most recent national survey of higher trainees in child and adolescent psychiatry suggests that the number of trainees receiving inadequate supervision is continuing to fall (Smart & Cottrell, 2000). Sharp and Morris focus instead on case-load and case mix and are to be commended for persevering over three annual cycles with an audit that clearly demonstrates changes being made in the light of data collected, followed by re-audit and re-evaluation – audit projects rarely ‘close the loop’ so clearly.
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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 | 0.018 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.067 | 0.045 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".