Competency Corner, Part Three: Practice-based Weightings of the CBPS
Bibliographic record
Abstract
The first two articles in this series outlined the task, research, terminology and processes the Competence Committee used to develop the Competency-based Performance Standards (CBPS). These standards were designed as a framework for revision of the Canadian Standard Assessment in Optometry (CSAO) to directly link the exam and practice requirements of Canadian Optometrists. This linkage required determination of the relative weight to be assigned to each of the various competencies. Working groups of practising optometrists rated the frequency and criticality of performance of each of the competencies using a standardized rating system. Results indicated that the majority of a revised CSAO would focus on providing comprehensive eye and vision care (78%), followed by management (11%) and collaboration (10%). The ratings also allowed calculation of the appropriate weighting of the underlying general attributes. The heaviest weighting was assigned to candidates’ professional optometric knowledge and the ability to apply this knowledge (41%), followed by communication (27%), planning (13%), ethics (11%) and self-directed learning (8%). The last article in this series will describe work to evaluate the competence-based weightings of the current CSAO and to describe plans for future versions of the CSAO.
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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 | 0.045 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".