Understanding Non-Technical Competencies: Compassion and Communication among Fourth-Year Veterinarians-in-Training
Bibliographic record
Abstract
Over the past several decades, non-technical competencies have been given an increasing amount of emphasis in veterinary medical training. However, additional research is needed to continue understanding the role that non-technical competencies play in veterinary success and wellness. An inter-related pair of non-technical competencies that needs further empirical investigation is communication and the influence of compassion on veterinarians. This research study investigated the relationship between compassion experiences and communication styles of fourth-year veterinarians-in-training using a canonical correlation analysis. The compassion fatigue resilience (CFR) model was the theoretical framework used to conceptualize how communication behaviors may contribute to compassion fatigue and compassion satisfaction. Compassion experiences were measured using a version of the Professional Quality of Life (ProQOL) scale. Communication style was measured using the Communication Styles Inventory (CSI). Results indicated that communication style is statistically significantly related to compassion experiences ( n = 281; Function 1, R c = .552, p < .001; Function 2, R c = .369, p < .001). Compassion fatigue was found to have a statistically significant association with the communication styles of emotionality ( r = .467, p < .001), impression manipulativeness ( r = .191, p = .001), and verbal aggressiveness ( r = .239, p = .001). Results indicated support for veterinary training programs to continue adapting their curricula to include communication training and intervention programs to address communication and compassion fatigue, as well as to consider how the relationship between these two constructs may influence the wellness and success of veterinarians-in-training and veterinarians. More research is needed to understand the role of impression manipulativeness in veterinary wellness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".