The relationship between competence and performance: implications for assessing practice performance
Bibliographic record
Abstract
OBJECTIVE: This paper aims to describe current views of the relationship between competence and performance and to delineate some of the implications of the distinctions between the two areas for the purpose of assessing doctors in practice. METHODS: During a 2-day closed session, the authors, using their wide experiences in this domain, defined the problem and the context, discussed the content and set up a new model. This was developed further by e-mail correspondence over a 6-month period. RESULTS: Competency-based assessments were defined as measures of what doctors do in testing situations, while performance-based assessments were defined as measures of what doctors do in practice. The distinction between competency-based and performance-based methods leads to a three-stage model for assessing doctors in practice. The first component of the model proposed is a screening test that would identify doctors at risk. Practitioners who 'pass' the screen would move on to a continuous quality improvement process aimed at raising the general level of performance. Practitioners deemed to be at risk would undergo a more detailed assessment process focused on rigorous testing, with poor performers targeted for remediation or removal from practice. CONCLUSION: We propose a new model, designated the Cambridge Model, which extends and refines Miller's pyramid. It inverts his pyramid, focuses exclusively on the top two tiers, and identifies performance as a product of competence, the influences of the individual (e.g. health, relationships), and the influences of the system (e.g. facilities, practice time). The model provides a basis for understanding and designing assessments of practice performance.
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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 | 0.024 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".