THE ROLE OF FIREFIGHTER HEALTH AS PREDICTORS OF WORK LIMITATIONS IN FIRE SERVICE
Bibliographic record
Abstract
Background: Firefighters (FFs) are exposed to life-threatening conditions while carrying out their tasks. These strenuous conditions predispose them to a high rate of disability, morbidity and mortality. These hazardous conditions put them at risk for work limitations, but the literature is scarce in this matter. Objective: The overarching objective of this thesis is to determine the role of FFs health as predictors of work limitations in fire service. Specifically, the first objective is to determine whether work limitations differ among FFs based on the location or number of painful sites. The secondary objective is to determine whether non-musculoskeletal comorbid health condition predict work limitations among FFs. Participants: A convenience sample of FFs between the age of 18-60 years working in the province of Ontario, Canada. Methods: Participants completed either an online or paper-based survey including a work limitation questionnaire(WLQ-26) and a self-reported comorbidity questionnaire. For the first objective, a one-way ANOVA and post hoc test was used to determine the differences in work limitations among FFs based on the number or location of painful sites. For the secondary objective, a univariate linear regression was used to evaluate whether non-MSK comorbid health condition predict work limitations among FFs. Results: FFs with three or more painful sites had more physical limitations than FFs without pain, (Mean difference=1.03/10; 95% CI: 0.16-1.62; p=0.02). FFs with pain at the region of the spine experienced more physical limitations compared to FFs with no pain (Mean difference=0.89/10; 95% CI: 0.17-1.62; p=0.007). For the secondary objective, univariate analysis showed that having at least one comorbidity(p=0.04) had a small, but significant association with greater mental work limitations (F2,316=2.94; p=0.05; R2=0.02). Separate univariate analysis showed that women FFs having one comorbidity had a small but significant association with physical (R2=0.07; F2,97=3.92, p=0.02) and mental work limitations (R2=0.04; F2,101=1.89, p=0.15) than women without non-MSK comorbidity. Conclusion: Having multiple painful sites or pain at the spinal region influenced work limitations among FFs. Also, non-MSK comorbidity impacted mental limitations among FFs; especially among women FFs. Therefore, managing MSK and on-MSK comorbidities is a necessary health care goal to prevent work limitations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".