How Do I Choose a Job? Factors Influencing the Career and Employment Decisions of Physiotherapy Graduates in Canada
Bibliographic record
Abstract
Purpose: Physiotherapy is a diverse profession: there are many areas in which physiotherapists can practise. New graduates must decide in which area of physiotherapy they would like to work and with which organizations to seek employment. The purpose of this study was to describe the factors that influenced the career (area of practice, practice setting) and employment (organization) decisions of recent physiotherapy graduates. Method: Given Canada’s vast expanse, we used survey methodology. We invited English-speaking physiotherapists who had completed their physiotherapy education between October 2015 and December 2017 to participate in this study. The survey was emailed to 1,442 physiotherapists in British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, and Nova Scotia. Results: We collected 351 responses (24%). Almost all respondents reported currently working as a physiotherapist, and the majority worked with patients with musculoskeletal conditions. Clinical education experiences were most influential in determining career decisions. Area of practice, practice setting, and mentorship were the most influential factors contributing to employment decisions. Conclusions: Clinical education experiences are influential in shaping physiotherapy students’ career and employment decisions. Employers who want to recruit physiotherapy graduates may consider partnering with physiotherapy programmes to offer clinical placement experiences and develop mentorship programmes that help build novice physiotherapists’ competence and confidence.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".