Burnout in Canadian Physiotherapists during COVID 19 - A National Survey
Bibliographic record
Abstract
Of 679 responses, the mean age was 46.3 (SD 10.5) years. The majority were female (75%) and 53% had dependents. 94% worked in clinical practice, 41% of whom worked in a general hospital and 24% in private practice. The majority (61%) reported a previous COVID infection. The mean number of reported positive strategies (e.g., hobbies, mental care, and reduced work hours) to manage stress was 2.4 (SD1.6) and 0.9 (SD1.1) for negative strategies (e.g., isolation, junk food consumption, and too much screen time). OLBI exhaustion mean score was 51.8 (SD 17.3) and 47.2 (SD 16.0) for disengagement. Female sex, increased years in practice, reported burnout since COVID, and a lower number of positive and negative habits were predictors of exhaustion, while those who reported burnout since COVID and a lower number of positive and negative habits were predictors of disengagement. 31% reported having a workplace program to manage stress or burnout while others indicated no program or did not know whether a program existed. The objective of this study was to evaluate pandemic-related burnout and engagement in Canadian physiotherapists. As the impacts and consequences of COVID-19 continue and physiotherapists experience workplace stress, more proactive steps are needed to minimize workplace stress and support emotional resilience. We used purposive, snowball sampling to recruit Canadian physiotherapists to complete an online survey. The survey (English and French) was administered between Sept 2022 and Jan 2023 and included questions related to demographic and practice variables, burnout (Oldenburg Burnout Inventory [OLBI]), and burnout features. Multiple regression analysis examined the impact of burnout features on OLBI constructs (exhaustion and disengagement.) Burnout during COVID is significant among physiotherapists with several factors impacting exhaustion and disengagement from work. Several positive strategies to mitigate burnout were reported. However, the majority of physiotherapists did not have access to or did not know about support services at work.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".