On the Design and Delivery of Human Anatomy Courses in Canadian Kinesiology Programs
Bibliographic record
Abstract
Human anatomy is a key subject area within undergraduate kinesiology programs. However, literature detailing the course structures, available resources, and teaching approaches used to deliver anatomy education in kinesiology programs is lacking. The present study sought to address this deficit by surveying instructors in Canadian university and college kinesiology programs regarding the anatomy courses they offer. The median (IQR) reported enrollment across 48 courses (40% response rate) was 165 (155) students and the mean (±SD) student/instructor ratio was 20:1 (±9) for the laboratory/tutorial sessions. Systemic or regional approaches to teaching were utilized in 39.6% of courses each, while 20.8% used both. Weekly contact times for in-person lectures and laboratories were 2.7 (±0.5) hours and 1.9 (±0.8) hours, respectively. Written assessments accounted for 64% (±20) of students' final grades, while practical tests accounted for 29% (±18). Joints and the skeletal, muscular, and nervous systems were the most covered units (>85% of courses). The most common resources available to students were atlases, plastic models, skeletons, and interactive software; human cadaver dissection was used in 7% of courses. Primary instructors for 95.5% of courses were permanent faculty/staff, but student assistants were utilized in half the courses, mostly for marking and laboratory activities. Overall, these results add considerably to the literature from allied health disciplines and fill a deficit in studies on the teaching methods and resources used for human anatomy education within Canadian undergraduate kinesiology programs. Accordingly, these findings may be used to inform curriculum design and best practices in anatomy course delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".