Exploring empathy levels among Canadian paramedic students
Bibliographic record
Abstract
Background: Empathy is an important factor in communication between healthcare provider and patient. Previous studies have shown that empathy benefits patient care in multiple ways. Empathy allows a space of decreased vulnerability and, as a result, builds trust in healthcare relationships. It fosters open communication that leads to improved patient care; improves patient satisfaction; and buffers healthcare provider burnout. This study aimed to determine the empathy levels demonstrated by paramedic students to patients with various medical conditions, and to compare these findings with those of previous studies. Methods: This study employed a cross-sectional design of a convenience sample of first and second year paramedic students in a community college programme in Ontario, Canada. The Medical Condition Regard Scale (MCRS) was used to measure empathy levels in these students across five medical conditions: physical disability, intellectual disability, suicide attempt, mental health emergency, and substance abuse. Results: A total of 43 students participated in the study; 27 males and 15 females (1 unknown). Males demonstrated a mean empathy score of 232.44 while females demonstrated a mean of 266.4. Across the five medical conditions, substance abuse had the lowest mean empathy score (42.88), followed by mental health emergency (49.58), suicide attempt (49.47), intellectual disability (50.42) and physical disability (53.0). Conclusion: Results from this study suggest that paramedic students demonstrated the lowest levels of empathy towards patients suffering from substance abuse issues, and the highest levels of empathy towards patients with a physical disability. Male paramedic students are less empathetic than their female peers, and second year paramedic students are less empathetic than their first year counterparts. These results provide an insight into paramedic student attitudes in Canada, and provide a foundation for further studies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".