Perspectives of North American firefighters on navigating interventions and healthcare choices for work-related shoulder disorders
Bibliographic record
Abstract
Background: The unpredictable nature of firefighting, characterized by lifting and carrying of heavy equipment, forceful upper body movements in confined spaces, and exposure to extreme conditions, predisposes firefighters (FFs) to a range of work-related shoulder disorders (WSDs). The unique occupational demand of firefighting underscores the need for targeted intervention and occupational health programs tailored to the demands of firefighting. Objective: To explore the: (1) Management strategies that FFs employ after WSDs (2) Needs and preferences of FFs with WSDs in relation to their occupational health and recovery. Methods: = 5) with an average age of 47 years, who experienced WSDs in their careers. Semi-structured one-on-one virtual interviews were conducted using online video conference software and were transcribed verbatim. Data was analyzed using thematic analysis. Result: Four themes emerged from firefighters as management strategies following WSDs: (1) Appropriate diagnostic precision and tailored management; (2) The critical role of early medical and physiotherapy intervention; (3) Comprehensive social support systems; (4) Adaptive coping mechanisms. Two themes also emerged as needs and preferences in relation to their occupational health and recovery: (1) Formal and targeted training exercise programs; (2) Mandatory health and wellness monitoring programs. Conclusion: The unique occupational demands of firefighting necessitate a multifaceted and holistic approach to shoulder injury management and prevention. This approach encourages the development of tailored intervention programs that address the specific challenges and perceived needs of firefighters with WSDs. Supplementary Information: The online version contains supplementary material available at 10.1186/s12982-025-00739-8.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".