Bibliographic record
Abstract
Suicide and non-fatal suicidal behavior are major public health problems across the world: approximately 1 million people worldwide die by suicide each year. In fact, the number of lives lost through suicide exceeds the number of deaths due to homicide and war combined. Beyond the tragedy of life lost, there is the devastating human cost to family, friends, and colleagues, a cost carried forward with lasting impacts and lifelong repercussions. Suicide is injurious, both deeply and widely. Several studies have identified a link between suicide and occupation (1), including the healthcare professions and our own profession. The rate of suicide in the veterinary profession has been pegged as close to twice that of the dental profession, more than twice that of the medical profession (2), and 4 times the rate in the general population (3). No matter where we live, what we do, and what our state of the world, we share the common experiences of joy and sadness, face strife and hardship, and struggle to meet life’s challenges. Sometimes “the stuff of life” can pile up, leaving us overwhelmed, depressed, and feeling alone. It can even push us over the edge to thoughts of suicide. The 2012 CVMA National Survey Results on the Wellness of Veterinarians (n = 769) found that 19% of respondents had seriously thought about suicide and 9% previously attempted suicide (4). Of those who had seriously thought about it (n = 135), 49% felt they were still at risk to repeat. The risk is real. The numbers are compelling. As Halliwell and Hoskin (2) indicate, “We must develop a greater awareness within the veterinary profession of the issue of suicide, and of the predisposing signs and of the warning signs. There is ample evidence that bringing these issues out into the open, rather than bottling them up, is of great assistance in preventing suicides.” Although the stigma associated with suicide has been an important barrier to discussing the issue (5), we need to open the dialogue in the hope that with increased awareness we can reduce the numbers — and stem the tragedy. It’s time we talk about it.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".