A qualitative exploration of the emotional experiences and applications of emotional intelligence in early‐career veterinarians
Bibliographic record
Abstract
BACKGROUND: Mental distress is a persistent and growing concern in the veterinary profession. The early-career period has been shown to be particularly challenging, which may threaten the longevity of the veterinarian's career. The highly emotional nature of veterinary work has been suggested to contribute to poor mental wellbeing. Emotional intelligence (EI) can help protect veterinarians from the negative effects of their career on their wellbeing. The objective of this study was to explore the emotional experiences of early-career veterinarians in clinical practice in Canada. METHODS: Twenty-one individual interviews with veterinarians who graduated between 2016 and 2023 were conducted over Zoom. The recordings were transcribed and analysed using template analysis. RESULTS: Three themes were identified in the analysis. First, client interactions were a source of emotional stress. Clients' financial stress and hostile comments exacerbated the participants' emotional distress. Second, most participants did not prioritise their emotional recognition and management. The veterinarians in this study indicated that having limited resources and time to process their emotions contributed to their lack of emotional recognition. Last, while participants appreciated EI as a skill, they described only selectively practising parts of EI, specifically empathy, which was perceived as the most relevant to client communication. LIMITATIONS: The results of this qualitative research are context specific. Readers are encouraged to carefully consider the context, research methodology and authors' positionality to make informed judgements on the application of the findings. CONCLUSION: Overall, the results highlight the impact of client interactions on veterinarians' emotional burden, veterinarians' shortcomings in self-emotional management, and opportunities to initiate or improve EI training. These findings suggest the need to explore ways to improve EI training, specifically in managing self-emotions, to enhance mental wellbeing in the profession.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".