Undergraduate paramedic students' empathy levels: A two-year longitudinal study
Bibliographic record
Abstract
Empathetic healthcare attitudes in patient care have been credited with increasing patient compliance, facilitating greater prognostic accuracy, enhancing patient satisfaction, reducing patient stress levels, minimising the rate of medical errors and achieving optimal physiological results. However, whether paramedic students have empathetic attitudes is largely unknown. Therefore, the objective of this study was to assess the extent of empathy in paramedic students over a two-year period from six Australian universities. This was a cross-sectional study employing a convenience sample of first, second, and third year undergraduate paramedic students during May 2011 and 2012. Student empathy levels were measured using the Jefferson Scale of Physician Empathy – Health Profession Students’ version (JSPE-HPS). A total of 1,719 students participated in the study of which 57% (n = 979) were females. The two-year overall JSPE-HPS mean was 105.92 ( SD = 12.85). Females had greater mean JSPE-HPS empathy scores than males 107.45 v 103.86 ( p < .0001, d = 0.28). Interestingly, JSPE-HPS empathy scores did not decline as students progressed through their degree ( p = .541). Results from this two-year study provide the paramedic discipline with important empirical evidence in its attempt to better understand the complex construct of empathy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".