Predicting Work Stress Burnout in Rural and Urban Emergency Medical Technicians Through the Use of Early Recollections
Bibliographic record
Abstract
Problem . Literature on work-stress burnout among emergency medical technicians (EMTs) suggests that they have maintained the same levels of burnout and attrition rates for the past 20 years. The purpose of this study was to investigate the relationship between early recollections and burnout in EMTs working in urban and rural locations. Method . A demographic questionnaire, the Staff Burnout Scale for Health Professionals (SBS-HP), and two early recollections, were used to survey 120 emergency medical technicians in Toronto, Ontario and Mojave County, Arizona to assess their level of burnout and to identify various themes in early recollections. Results . The results from the analysis of the data from general demographic information, the SBS-HP, and the early recollections indicated that urban EMTs experienced higher levels of burnout than rural EMTs. No significant findings were found to correlate with any of the eight early recollection themes to global burnout levels, or the four sub-scales of the SBS-HP. Conclusions . As a result of the study the following conclusion was drawn: that EMTs who work in urban areas experience higher levels of burnout than those EMTs in rural areas.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".