Is Compressed and Limited Synchronous Delivery of Anatomy Content in a Hybrid Delivery Format Effective in Transitional OT Student Learning?
Bibliographic record
Abstract
Hybrid occupational therapy (OT) students transitioning from certified OT assistants (COTAs) to OTs can successfully learn graduate-level anatomy in a compressed format with limited synchronous instruction time. The effectiveness of a human anatomy course with limited synchronous instruction time for transitional hybrid occupational therapy students was investigated. A retrospective, non-randomized study was used. A university graduate level human anatomy course for transitional OT students used prosected (previously dissected) cadavers. Students (n=46, 32 instruction hours over 16 weeks) final anatomy course grades for three cohorts were measured retrospectively. There was a 98% first-time pass rate and 100% second time pass rate. Less than 5% of the students needed to either repeat the course (one student) or withdrew from the course prior to course completion (one student). Results suggest that a hybrid learning model with limited synchronous instruction time is effective for transitional OT students learning human anatomy. Programs should consider how instruction time and distribution impacts anatomy learners, and when there is limited time in the classroom, investigate alternative pedagogies for those few students who would benefit from a more immersive-learning environment. Anatomy knowledge is essential in progressing through occupational therapy curriculums and is needed for client management. Understanding what factors impact learning anatomy could assist in creating more effective anatomy courses for occupational therapy students.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".