Aircraft-assisted suicide: The rarity of attempts, ideation, or underreporting?
Bibliographic record
Abstract
Introduction: Aircraft-assisted suicide is a rare but serious event, with immediate consequences for the pilot, crew, and passengers. The overt linkage between mental illness and suicidal behaviour is well-known, however, the infrequency of these events in the context of aviation, coupled with poor record-keeping and reluctance to disclose, likely disguises the true extent of mental illness among pilots. One critical research gap in Canada has been the lack of investigation of crashes linked to suicide or a mental disorder. Research on aircraft-assisted suicide will address the key issues of pilots’ reluctance to disclose, report, and seek mental health services. Our study investigates the frequency of aircraft-assisted suicide in Canada, reviews current medical standards, and discusses preventative interventions to manage future risk. Materials and methods: Our study examined investigations and reports conducted by the Transport Safety Board to explore the frequency of aircraft-assisted suicide over a ten year period. Results: Aircraft-assisted suicides in Canada are rare, however, mentally ill pilots involved in fatal crashes are likely underestimated. Our study highlights key barriers in aviators’ disclosure of mental health symptoms, an ineffective screening process, and a consequence-based system that deters pilots from their duty to report. Discussion: our investigative analysis addresses key limitations in screening of mentally ill pilots, explores current medical standards and aeromedical exams, infrequency of fitness assessments, and demonstrates the critical need for continuous evaluation of pilots in this safety-sensitive occupation. Conclusion: The current paper addresses the need for continuous evaluation of pilot’s mental health and a more rigorous screening process to accurately identify suicide risk in pilots and prevent aircraft-assisted suicide.
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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 | 0.033 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".