Bibliographic record
Abstract
David Bartram and David Baldwin comment: We are grateful for the opportunity to respond to Desmond Rice's comments on our hypothetical model to explain the risk of suicide in veterinary surgeons. A recent meta-analysis of 44 studies demonstrated that emotional intelligence (EI) is positively associated with mental health (Schutte and others 2007). Although the methodologies of the meta-analysis and the studies on which it is based do not provide evidence regarding causality, it may be that the better perception, understanding, and management of emotion of individuals with higher EI make it less likely that they will experience mental health problems. There is also a strong positive correlation between several dimensions of EI and academic achievement (for example, Parker and others 2004, Petrides and others 2004, Austin and others 2005), so it seems unlikely that the current veterinary undergraduate admissions procedure inevitably selects a high proportion of students with low EI. However, EI may have a place in veterinary education (Timmins 2006), both to improve the veterinary surgeon-client relationship and as a possible buffer against stress in the profession, and the subject is worthy of further research. A number of the 'human givens' to which Rice refers are measured in the standard psychometric instruments used in our recent cross-sectional study of mental health and wellbeing in the UK veterinary profession (Bartram and Baldwin 2007). The questionnaire was mailed to a random stratified sample of 3200 veterinary surgeons in the UK and a response rate of over 56 per cent was achieved. We are grateful to all those who responded. Data analysis is underway and it is anticipated that preliminary results will be available by the autumn.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.032 | 0.034 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".