Suicides of famous chefs of today: stories and reasons
Bibliographic record
Abstract
Suicide and suicidal behavior are one of the key issues for public policy and health care. Well-known American psychiatrist Somya Abubucker states that according to the WHO, a death by suicide occurs every 40 seconds in the world and “there are indications that for every adult person who died by suicide, there could be more than twenty others who attempted to commit it”. Restaurants remain a highly stressful environment, with the chef profession ranking among the top 10 most nerve-racking jobs in the world. The aim of the article is to present the life story and professional activity of the world’s most famous chefs and restaurateurs who committed suicide over the past 30 years, as well as the analysis of the probable reasons for their actions. To achieve the declared goal, the following research methods were used: historical, descriptive, comparative, systematization and generalization. The chefs in question used to work in the best restaurants in the United States, Canada, France, Italy, Switzerland, Great Britain and Australia. They made a significant contribution to the development of world gastronomy, their establishments have been transformed into truly cult locations, awarded stars from «Michelin» and evaluations from «Gault & Millau», which have been operating for several decades and offering the most exquisite dishes. It was established that the reasons for their suicides were mostly depression, emotional exhaustion and burnout, economic crisis, a drop in profits and lower ratings, loss of a loved one / close person, influence of reality shows, constant pressure, irregular work schedule, unjustified accusations. It was found that among the methods of suicide, it is, in particular, hanging, use of firearms, drowning and suffocation, medicine and drug overdose. The motives of the actions of some of them remained a mystery, since not even suicide notes were found. Priorities for suicide prevention include increasing specialized mental health programs, restricting access to weapons, and strengthening surveillance to detect signals / reports / information that a person is exhibiting suicidal intent. The obtained results make it possible to supplement and expand specialized training courses in higher education institutions of Ukraine, where personnel are trained for the field of tourism, hotel and restaurant industry, as well as medicine (in particular, psychology, psychiatry, addictionology and rehabilitation).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".