A place to call our home: innovative rural physical therapy training in Canada
Bibliographic record
Abstract
BACKGROUND: To promote rural practice and increase enrollment in the entry-level Master of Science Physical Therapy program at the University of Alberta, the Department of Physical Therapy developed a rural physical therapy satellite campus located in a farming region in central Alberta. A distributed learning format was used to connect the rural cohort to the main urban campus. Real time video conferencing was used to connect the two campuses for all lectures, seminars and clinical skills classes. This evaluation aimed to describe a unique rural training program for physical therapy students and its effectiveness in promoting work in rural communities after graduation. METHODS: Physical Therapy students in the first three years (2012-2015) of commencing the rural satellite program (n = 280) were surveyed, and six focus groups were held to capture student experiences, satisfaction and engagement. Data were collected on employment locations of the 2012-2019 graduates' first physical therapy position and current employment. RESULTS: Survey results suggested comparable levels of satisfaction and engagement for all physical therapy students regardless of campus. Focus group data revealed that students quickly accepted the distributed learning technological interface, enjoyed their local campuses, and felt connected to instructors and student colleagues. Compared to the overall physical therapy workforce, a higher percentage of physical therapists graduating from the rural campus reported working in rural centers for both their first and current jobs. CONCLUSION: Regardless of campus, students were satisfied and equally engaged in the physical therapy program. Students who completed the physical therapy program in a rural setting tended to work rurally after graduation. A distributed learning model may be useful for other healthcare training programs to promote engagement in rural health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".