Exploring Technology Integration in Canadian Athletic Therapy Education
Bibliographic record
Abstract
There are many potential educational goals for using digital technologies in health professional education programs. Previous studies have suggested that technology can be used in these settings to facilitate knowledge acquisition, improve clinical decision making, improve psychomotor skill coordination, and practice rare or critical scenarios. However, when using technology for educational purposes, many educators do not consider the resulting pedagogical implications of using these tools to teach course content. The purpose of this study was to explore this phenomenon in a sample of athletic therapy educators, by investigating their views and attitudes towards using digital technologies in athletic therapy specific courses. Researchers used a sequential explanatory mixed-methods approach (via questionnaire and individual interviews) to explore this topic. It was found that the majority of athletic therapy educators in this sample (n = 21) did not in fact consider the pedagogical implications of technology integration and moreover used technology in rudimentary fashions (e.g., to deliver course content or to provide additional context to explain a topic). Conversely, those educators with higher levels of pedagogical and technological knowledge appeared to use technology in more constructive ways while considering the pedagogical impact of their technology integration decisions. Although this study focused on athletic therapy education, the findings are not unique to this discipline. Carefully designed, pedagogically-sound technologies have very specific and useful ways of empowering learning and have the potential to achieve many educational goals for any educator.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".