“I Can’t Crack the Code”: What Suicide Notes Teach Us about Experiences with Mental Illness and Mental Health Care
Bibliographic record
Abstract
OBJECTIVE: While mental illness is a risk factor for suicidal behaviour and many suicide decedents receive mental health care prior to death, there is a comparative lack of research that explores their experiences of mental illness and care. Suicide notes offer unique insight into these subjective experiences. Our study explores the following questions: "How are mental illness and mental health care experienced by suicide decedents who leave suicide notes?" and "What role do these experiences play in their paths to suicide?" METHOD: We used a constructivist grounded theory framework to select a focus of qualitative analysis and engage in line-by-line open coding, axial coding, and theorizing of the data. Our sample is a set of 36 suicide notes that explicitly make mention of mental illness and/or mental health care, purposefully selected from a larger sample of 252 notes. RESULTS: The primary themes from our sample were 1) negotiating personal agency in the context of mental illness, 2) conflict between self and illness, and 3) experiences of mental health treatment leading to hopelessness and self-blame. These experiences with mental illness and mental health care can give rise to exhaustion and a desire to exercise personal agency, contributing to suicidal behaviour. CONCLUSIONS AND RELEVANCE: This study highlights unique perspectives by suicide decedents, whose voices and experiences may not have been heard otherwise, addressing a critical deficit in existing literature. These insights can potentially enrich clinical care or strengthen existing suicide prevention programs.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".