Usefulness of CanMEDS Competencies for Chiropractic Graduate Education in Europe
Bibliographic record
Abstract
PURPOSE: In 2008, the European Academy of Chiropractic decided to develop a competency-based model for graduate education in Europe. The CanMEDS (Canadian Medical Education Directives for Specialists) framework describes seven competency roles (fields) and key competencies identified as fundamental to all specialist doctors. It was not known how these fields are perceived by chiropractors in Europe. The purpose of this study was to compare perception scores of senior chiropractic as well as medical students with perception scores of licensed chiropractors and to analyze practitioners' remembered confidence in these competency fields. METHODS: An anonymous 5-point Likert scale electronic questionnaire was sent to senior students of two chiropractic schools and licensed chiropractors of five European nations. Age and gender differences as well as differences in appraisal of the competencies in respect to importance and remembered confidence were analyzed. RESULTS: Response rates were low to moderate. Agreement of importance of the seven competencies was not different between chiropractic and medical students as well as licensed chiropractors. Chiropractic students and chiropractors regarded all key competencies as important (averages >/=4.0). The importance versus remembered confidence was consistently judged higher by about 1/2 point on the 5-point scale, significant for all competency fields (p < .001). CONCLUSION: The seven competency fields seem to be of the same importance for chiropractic senior students and licensed chiropractors and might be considered as a base for future graduate training in chiropractic. The survey should be replicated with additional samples and further information should be gathered to reflect reality.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".