Health Information on Firefighter Websites: Structured Analysis
Bibliographic record
Abstract
BACKGROUND: Owing to the fact that firefighters have unique health risks, access to firefighter-specific internet-based health information is a potential mechanism for achieving better health and work outcomes. OBJECTIVE: The objective of our study was to identify the amount and nature of health information resources available on Canadian firefighter-specific websites and the extent to which resources are consistent across websites as a surrogate indicator of diffusion of information. METHODS: A search of health resources on firefighter websites (union and employer) for all Canadian provinces, major cities and a subset of smaller cities, and the International Association of Fire Fighters (IAFF) website was conducted on Google (July 2017). Content was identified and classified based on the type of resource, health focus, and location. The quantity and nature of the resources were summarized using descriptive statistics. RESULTS: Among all (N=313) websites reviewed, 41 websites had health information with a cumulative total of 128 resources that addressed firefighter mental (59/128, 46.1%), physical (43/128, 33.6%), and work health (26/128, 20.3%). The highest density of information was found on international and national websites (13 resources per website) and the least on local websites (1 resource per 7 websites). Three provinces (Ontario, Québec, and British Columbia) hosted 81% (65/80) of the provincial, territorial and local resources. General mental health (20/59, 34%), posttraumatic stress disorder (14/59, 24%), and suicide (14/59, 24%) were the most prevalent topics within the mental health resources, whereas half (21/43, 49%) of all physical health resources were on cancer. No resources from Northern Canada were found. Musculoskeletal health was not mentioned in any of the resources identified. There was minimal cross-linking of resources across sites (only 4 resources were duplicated across sites), and there was no clear indication of how the content was vetted or evaluated for quality. CONCLUSIONS: There was wide variation in the amount and type of information available on different firefighter websites with limited diffusion of information across jurisdictions. Quality evaluation and coordination of resources should be considered to enhance firefighters' access to quality health information to meet their specific needs. Mental health and cancer information aligned with high rates of these health problems in firefighters, whereas the lack of information on musculoskeletal health was discordant with their high rate of work injury claims for these problems.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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; both teacher heads agree on what is shown here.
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".