Bibliographic record
Abstract
This thesis proposes a resilience assessment model to assess the physiological changes experienced during a simulated firefighting environment. Currently, a resilience assessment model that measures the response to job stressors does not exist. The model provides a mechanism to measure the level of progress or decline experienced by public safety personnel, while exposed to job-related stressors. The research design for the model includes analysis of heart rate and electrocardiogram-based RR intervals at baseline and during the training activity. The model was applied to a case study that involved an extreme heat (50?? C) search and rescue training task for pre-service firefighters (mean age of 21 years and a standard deviation of 4.6 years with 20% female). The performance of the female participants revealed a 54% higher standard deviation of normal R-R interval (SDNN) score compared to male participants. Furthermore, the average of the mean heart rate (HR) for males was 18% higher than the females. The SDNN for 93% (39) of males was less than 100ms as compared to the female SDNN where only 22% (2) of participants fell below 100 ms. Applying the model revealed female participants to be more resilient than their male counterparts. Prior studies have not addressed biological sex differences in their studies and this needs to be further explored. Implementation of the resilience assessment model to assess and improve the effectiveness of resilience training programs could benefit firefighters by reducing the incidence of PTSD, depression and workplace injuries.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".