Practical guidance for firefighter applicants preparing for cardiorespiratory fitness testing: a secondary analysis of self-reported physical activity levels
Bibliographic record
Abstract
Adequate cardiorespiratory fitness is critical for firefighters since an insufficient level of fitness threatens the integrity of their operations and could be dangerous for their lives. In fact, the leading cause of mortality for on-duty firefighters is not injury but sudden cardiac death. Therefore, to mitigate these risks, potential firefighter recruits are often required to perform a graded exercise test to determine their cardiorespiratory fitness as part of the recruitment process. However, there are currently limited data available to prospective firefighters on the amounts and types of exercises needed to be successful in the graded exercise test, commonly known as a V̇O 2max test. Physiological parameters for the current secondary analysis were collected on firefighter applicants who performed the graded exercise test where 72% were successful and 28% were unsuccessful to meet the minimum standard set at 42.5 ml kg −1 min −1 . Prior to their test, applicants were asked to describe their exercise training routine by indicating the number of minutes per week spent exercising. Activities were then divided into one of two categories: endurance exercise or strength and power exercise training. The total exercise training describes the sum of all activities performed each week. The sum of endurance exercise activities and the sum of strength and power exercise activities were compared between the successful and the unsuccessful groups and results showed that successful applicants had a higher training volume and performed more endurance exercise training as compared to unsuccessful applicants. Therefore, practical recommendations related to exercise training regime are presented for firefighter applicants to embrace as guidance to prepare for their graded exercise test as part of their recruitment process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".