Assessing the competence of practicing physicians in New Zealand, Canada, and the United Kingdom: progress and problems.
Bibliographic record
Abstract
Members of the public expect practicing physicians to be competent. They expect poorly performing physicians to be identified and either helped or removed from practice. "Maintenance of professional standards" by continuing education does not identify the poorly performing physician; assessment of clinical performance is necessary for that. Assessment may be responsive-ie, following a complaint- or periodic, either for all physicians or for an identified high-risk group. A thorough review using a range of tools is appropriate for a responsive assessment but is not practical for periodic assessment for all. A single, valid, reliable, and practical screening tool has yet to be devised to identify physicians whose practice is suboptimal. Further, articulate commentators are concerned about the harm that too-intensive scrutiny of professional performance may cause. We conclude that high performance by all physicians throughout their careers cannot be fully ensured, but it is nonetheless the responsibility of licensing bodies to use reasonable methods to determine whether performance remains acceptable. Such methods should be shown scientifically to be accurate, valid, and reliable for practicing physicians. Such an approach is likely to encourage the agreement and cooperation of the profession. To do less risks losing the trust of the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".