Last Words: Are There Differences in Psychosocial and Clinical Antecedents Among Suicide Decedents Who Leave E‐Notes, Paper Notes, or No Note?
Bibliographic record
Abstract
OBJECTIVE: Only a minority of suicide decedents leave a suicide note. Typically, the notes are handwritten on paper; however, electronic suicide notes have been reported with increasing frequency. This emerging phenomenon remains generally under-researched. The aim of this study was to compare the psychosocial and clinical antecedents of suicide decedents who left E-notes with those who left paper notes or no notes. METHOD: The study was embedded in the Southwestern Ontario Suicide Study (SOSS). The SOSS was a three-year case series of consecutive deaths by suicide that occurred in the region between 2012 and 2014. Data on psychosocial and clinical antecedents were collected with a modified version of the Manchester questionnaire used in the UK. RESULTS: Of the 476 suicides files reviewed, 45.8% contained a suicide note. A total of 383 separate suicide notes were left: 74.3% were paper notes and 25.7% were E-notes. The results of the multivariate regression analyses indicate that the likelihood of leaving a suicide note was negatively associated with a history of admissions to a mental health unit, while the likelihood of leaving an E-note was negatively associated with age, positively associated with presence of a mental disorder, and negatively associated with history of hospital admissions. CONCLUSIONS: Future studies with larger samples need to consider the timing of the text messages, and appraise whether there was the intent of seeking help or rescue in the text messages.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".