Firefighter Staffing Model Implications on Fire Casualties and Fire Loss: Life Safety and Socio-Economic Impacts of the Fire Service
Bibliographic record
Abstract
Fire and rescue services are considered a staple among services provided by governments to local communities. Local governments are often charged with providing these services, especially across the United States and Canada. As with any professional service, there are standards set forth in order to ensure services are adequate and provide equity to the citizens that they serve. The purpose of this dissertation will be to delve into the common staffing configurations of career fire departments across the United States and Canada, particularly related to staffing levels on fire engines and ladder trucks. Fire departments utilize various staffing models, but commonly, fire engines and ladder trucks have complements of three or four firefighter crews in career departments in the United States and Canada. Industry standards suggests that a minimum of four firefighters should be staffed on each of these apparatus types. However, as a standard, there is flexibility for local departments to staff according to need, whether based on fiscal need or service demand. This dissertation examines correlations between staffing fire engines and ladder trucks with three personnel and higher property loss, as well as greater numbers of human casualties related to fire, verses communities that staff these apparatuses with four personnel. Data was collected from career fire departments across the United States and Canada, then statistically analyzed to determine if there was a correlation of lower staffing and higher property loss and greater human casualties as the result of fire incidents. The results illustrated some surprise findings where it is questionable if staffing levels impact fire loss and human casualties.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".