Doctors whose competence has been reviewed under the Medical Practitioners Act 1995
Bibliographic record
Abstract
Aim An analysis of referrals for competence reviews since the introduction of the Medical Practitioners Act 1995. Methods A retrospective file review at the Medical Council of New Zealand. Results In the six years to 1 July 2002, 258 doctors were referred. The characteristics of the referrals and the concerns, and the demographics of the doctors are presented. Competence reviews were ordered for 114 doctors and carried out for 92, of whom 23 did not meet the standard. The demographics of this group are also presented. Conclusions Of the competence concerns received by the Council, less than half result in a decision to review the doctor’s competence, and of these only one quarter led to the finding that the doctor did not to meet the required standard. The characteristics of this group appear to match overseas findings, but the numbers in New Zealand are small. The cost of uncovering this small group is significant. The Medical Practitioners Act 1995 introduced measures to require the Medical Council of New Zealand (‘the Council’) to ensure the continuing competence of medical practitioners.1 We have described the process by which the Council does this elsewhere.2 When the Council receives a concern from a Complaints Assessment Committee it is obliged to undertake a competence review. In all other cases it has discretion, and in these cases the decision is made on the Council’s perception of the merit of the concern, and the doctor’s written response. This paper examines the characteristics of the doctors concerned and the review
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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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".