Curricula within Ontario universities as it relates to the profession of kinesiology
Bibliographic record
Abstract
An undergraduate degree in kinesiology is one of the requirements to become a registered kinesiologist in Ontario, Canada. This study examined the alignment among 31 four-year honours degrees offered at 18 post-secondary institutions across Ontario and competencies associated with the profession of kinesiology. Curricula were analyzed against 14 essential competencies set by the College of Kinesiologists of Ontario and nine additional competencies related to the practice of kinesiology. The type of course (core or elective), presence of a lab, and lab hours per week were recorded. All degrees evaluated covered 79% (11/14) of the essential competencies and 33% (3/9) of the additional competencies. Notably, only five essential competencies and no additional competencies were universally met through core coursework alone; coverage occurred via core courses, electives, or a combination. Lab components were consistently associated with anatomy, biomechanics, exercise physiology, and assessment courses. Hands-on training hours, especially placement/clinical experience, varied significantly among degrees. Overall, these data highlight curricular breadth and variability in kinesiology degrees, and the diversity of elective choices to prepare students for the range of opportunities available to them after graduation. Kinesiology as a profession has a broad scope of practice and kinesiology degrees are not directly aligned with this role.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".