Being Pro-Active in Meeting the Needs of Suicide-Bereaved Survivors: results from a systematic audit in Montreal
Bibliographic record
Abstract
Abstract Background Suicide is a major public health concern that affects some 3,500 individuals a year in Canada. According to the literature, each suicide affects an average of six people. The aim of this study was to describe the met and unmet needs of suicide-bereaved survivors and to formulate postvention recommendations. Methods In the context of an exploratory mixed-methods audit of 39 suicides that occurred in Montreal (Canada) in 2016, suicide-bereaved survivors ( n = 29) participated in semi-structured interviews and completed instruments to assess pathological grief, depression (PHQ-9), and anxiety (GAD-7). Results Mean age of participants was 57.7 years; 23 were women. Although help was offered initially, in most cases by a health professional or service provider (16/29), 22 survivors would have liked to be contacted by telephone in the first two months post suicide. Four categories of individual unmet needs (medical/pharmacological, information, support, and outreach) and one collective unmet need (suicide pre/postvention training and delivery) emerged. Conclusions Although there have been provincial initiatives in favor of suicide-bereaved survivors in the past decade, many dwindled over time and none has been applied systematically. Recommendations for different stakeholders (Ministry of Health and Social Services, coroners, NGOs, and representatives of suicide-bereaved survivors) outlined in this study could be an interesting first step to help develop a provincial suicide pre/postvention strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".