A Cross-Sectional Study Investigating Burnout in Individuals With a Physiotherapy Licence in Manitoba, Canada
Bibliographic record
Abstract
Purpose: There is little published research from Canada that has examined burnout in individuals with a physiotherapy licence. Our purpose was to identify the extent of burnout, factors predicting burnout, perceived effects of burnout, and strategies to mitigate burnout in physiotherapists in Manitoba, Canada. Method: We conducted a cross-sectional online survey of individuals with a physiotherapy licence in Manitoba. We completed descriptive analysis, regression statistics (odds ratio, 95% CI), and an inductive analysis of open-ended responses. Statistical significance was set at p ≤ 0.05. Results: The response rate was 21.9% ( N = 238). Many respondents (114, 47.9%) were burnt out/at risk for burnout. Respondents working in private practice were less likely than those in public practice to experience burnout/at risk for burnout (OR 0.35 [0.18, 0.68]). Respondents strongly agreed that burnout affected physiotherapists, patients, and the place of employment. Strategies specific to the workplace and individuals were identified to mitigate burnout. Conclusion: Many survey respondents with a physiotherapy licence in Manitoba, Canada, were burnt out or at risk for burnout, which may have several implications, such as strain on personal and work relationships and lower quality of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".