Development of a new examination for the Canadian Chiropractic Examining Board
Bibliographic record
Abstract
OBJECTIVE: Since 1963 the Canadian Chiropractic Examining Board has conducted competency examinations for individuals seeking licensure to practice chiropractic in Canada. To maintain currency with changes in practice, examination content and methodology have been regularly updated since that time. This paper describes the process used by the Canadian Chiropractic Examining Board to restructure the examination to ensure it was current and to align it with the 2018 Federation of Canadian Chiropractic's Canadian Chiropractic Entry-to-Practice Competency Profile. METHODS: A subject-matter-expert committee developed proposed candidate outcomes (indicators) for a new examination, derived from the competency profile. A national survey of practice was undertaken to determine the importance and frequency-of-use of the profile's enabling competencies. Survey results, together with other practice-based data and further subject-matter-expert input, were used to validate indicators and to create a new structure for the examination. RESULTS: The new examination is a combination of single-focus and case-based multiple-choice questions, and OSCE (objective, structured, clinical examination) methodology. Content mapping and item weighting were determined by a blueprinting committee and are provided. CONCLUSION: Administration of the new examination commenced in early 2024.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".