Towards an acceptance of performance assessment
Bibliographic record
Abstract
The utility of any assessment tool critically depends on its level of acceptance by those on whom the assessment impacts. Performance assessment impacts on three distinct groups: patients/consumers, doctors and employers. While these groups may have conflicting beliefs and expectations of performance assessment, the process must be made acceptable to all. This can happen through an exploration of the beliefs and wishes of the key stakeholders in relation to performance assessment, together with the potential rewards and costs. This paper draws on the psychology literature in describing an effective model for change management. It outlines some strategies for each of the three key elements of any successful strategy for change, i.e. getting started, facilitating the transition and ensuring consolidation. Such a practical approach will foster the acceptance of performance assessment structures among all stakeholders.
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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 | 0.106 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.012 | 0.023 |
| 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".