Building personal resilience in paramedic students
Bibliographic record
Abstract
The present study examined the impact of a 6- to 8-hour, self-paced online resiliency training program to help students training to be Primary Care Paramedics (PCP) mitigate the risks associated with working in a trauma informed work setting. Of the 138 participants, 88 were male and 30 were female, with a mean age of 25.5 years. Of these, 81 students participated in the experimental group (who took the course), and 57 in the control group. Baseline demographic results were examined using bivariate comparisons between the control and experimental, and all were found to be statistically insignificant at p < 0.05 which suggests that there were no differences between the two groups on the pre-test demographic variables. Prior to the intervention there were no significant differences in total resilience or any of the sub-scales (selfreliance, meaningfulness, equanimity, perseverance, and existential aloneness). Following the resiliency training and the practicum experience, the experimental group scored better in total resilience and each of the sub-scores (p < 0.05) except meaningfulness. Results suggest that developing skills to mitigate and manage workplace trauma can reduce or help mitigate the negative impact of exposure to trauma and potentially reduce the risk of developing trauma related mental health problems which may impact the well-being and quality of life of students once employed as a paramedic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".