Exploring Mentorship from the Perspective of Physiotherapy Mentors in Canada
Bibliographic record
Abstract
Purpose: This study explored the factors that influence mentors in the profession of physiotherapy (PT) in Canada when engaging in a mentorship relationship. Methods: We conducted a quantitative, cross-sectional, Web-based survey. The target population consisted of Canadian physiotherapists who had experience as mentors. We used a modified Dillman approach to disseminate an online questionnaire to members of the Canadian Physiotherapy Association and its divisions using their respective e-blasts. We collected data on the nature and extent, facilitators, barriers, and benefits of mentorship and then analyzed them using descriptive statistics. Results: A total of 302 respondents were included in this study. They reported being a mentor to fellow PT colleagues (91%), undergraduate students (85%), graduate students (64%), and inter-professional colleagues (64%). We found that although many factors facilitated the respondents' ability to mentor, barriers to mentorship had minimal impact. Responses also reflected many perceived benefits of mentorship. Conclusions: This study provides novel evidence relating to the experience of mentorship from the perspective of mentors in the profession of PT. It reinforces the literature by highlighting the positive aspects of mentorship, and it underscores the continued need for support from professional associations, institutions, and physiotherapists to improve current mentorship experiences in PT.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".