When enough is enough: a conceptual basis for fair and defensible practice performance assessment
Bibliographic record
Abstract
INTRODUCTION: An essential element of practice performance assessment involves combining the results of various procedures in order to see the whole picture. This must be derived from both objective and subjective assessment, as well as a combination of quantitative and qualitative assessment procedures. Because of the severe consequences an assessment of practice performance may have, it is essential that the procedure is both defensible to the stakeholders and fair in that it distinguishes well between good performers and underperformers. LESSONS FROM COMPETENCE ASSESSMENT: Large samples of behaviour are always necessary because of the domain specificity of competence and performance. The test content is considerably more important in determining which competency is being measured than the test format, and it is important to recognise that the process of problem-solving process is more idiosyncratic than its outcome. It is advisable to add some structure to the assessment but to refrain from over-structuring, as this tends to trivialise the measurement. IMPLICATIONS FOR PRACTICE PERFORMANCE ASSESSMENT: A practice performance assessment should use multiple instruments. The reproducibility of subjective parts should not be increased by over-structuring, but by sampling through sources of bias. As many sources of bias may exist, sampling through all of them may not prove feasible. Therefore, a more project-orientated approach is suggested using a range of instruments. At various timepoints during any assessment with a particular instrument, questions should be raised as to whether the sampling is sufficient with respect to the quantity and quality of the observations, and whether the totality of assessments across instruments is sufficient to see 'the whole picture'. This policy is embedded within a larger organisational and health care context.
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 | 0.247 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.007 |
| Science and technology studies | 0.014 | 0.152 |
| Scholarly communication | 0.032 | 0.044 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".