Examining the quality of work-life of paramedics in northern Ontario, Canada: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Paramedics are exposed to multiple stressors in the workplace. They are more likely to develop occupational-related stress conditions compared to other occupations. This study focused on understanding the factors affecting QoWL of paramedics in northern Ontario, Canada; a particular focus was on understanding the personal and organizational factors, such as practicing community paramedicine (CP), which may be associated with Quality of Work Life (QoWL). METHODS: Paramedic QoWL was assessed using an online survey that was distributed to approximately 879 paramedics across northern Ontario. The survey included the 23-Item Work- Related Quality of Work Life Scale. Data analysis involved linear regressions with nine predictor variables deemed to be related to QoWL for paramedics with QoWL and its six subscales as dependent variables. Multiple linear regressions were used to assess the personal and organizational factors, such as practicing of CP, which predicted QoWL. RESULTS: One hundred and ninety-seven paramedics completed the questionnaire. Overall, the mean QoWL score of all paramedic participants was 73.99, and this average compared to relevant published norms for other occupations. Factors that were most associated with higher QoWL were, experience practicing CP (p < 0.05), number of sick days/year (p < 0.01), and higher self- rated mental health (p < 0.001). CONCLUSIONS: Higher paramedic QoWL appears to be associated with many factors such as number of sick days per year, self-rated mental health, and participation in CP. EMS organizations should consider establishing necessary workplace health promotion strategies that are targeted at improving QoWL for paramedics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".