Assessing the performance of doctors in teams and systems
Bibliographic record
Abstract
INTRODUCTION: Increasing attention is being directed towards finding ways of assessing how well doctors perform in clinical practice. Current approaches rely on strategies directed at individuals only, but, in real life, doctors' work is characterised by multiple complex professional interactions. These interactions involve different kinds of teams and are embedded within the overall context and systems of care. In addition to individual factors, therefore, we propose that the performance of doctors in health care teams and systems will also impact on the overall quality of patient care. Assessing these dimensions, however, poses a number of challenges. STRATEGIES: Taking a profile of a National Health Service, UK surgeon as an example, the team structures to which he or she may relate are illustrated. These include formal teams such as those found in the operating theatre, and those formed through various professional and collegial partnerships. The authors then propose a model for assessing doctors' performances in teams and systems, which incorporates the educational principles of continuous feedback to enhance future performance. DISCUSSION: To implement the proposed model, a wide range of professional, educational and regulatory bodies must collaborate. This raises a number of important implications for the future roles and relationships of these bodies, which are discussed. A strong and constructive partnership will be essential if the full potential of a more inclusive and representative assessment approach is to be realised.
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".