Perceptions of the Use of Alcohol and Drugs after Sudden Bereavement by Unnatural Causes: Analysis of Online Qualitative Data
Bibliographic record
Abstract
Bereavement is associated with an increased risk of psychiatric morbidity and all-cause mortality, particularly in younger people and after unnatural deaths. Substance misuse is implicated but little research has investigated patterns of drug or alcohol use after bereavement. We used a national online survey to collect qualitative data describing whether and how substance use changes after sudden bereavement. We conducted thematic analysis of free-text responses to a question probing use of alcohol and drugs after the sudden unnatural (non-suicide) death of a family member or a close friend. We analysed data from 243 adults in British Higher Education Institutions aged 18-40, identifying two main themes describing post-bereavement alcohol or drug use: (1) sense of control over use of drugs or alcohol (loss of control versus self-discipline), (2) harnessing the specific effects of drugs or alcohol. Across themes we identified age patterning in relation to substance misuse as a form of rebellion among those bereaved in childhood, and gender patterning in relation to men using alcohol to help express their emotions. The limitations of our sampling mean that these findings may not be generalizable from highly-educated settings to young people in the general population. Our findings describe how some young bereaved adults use drugs and alcohol to help them cope with traumatic loss, and suggest how clinicians might respond to any difficulties controlling substance use.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".