The Impact of Suicide Bereavement on Educational and Occupational Functioning: A Qualitative Study of 460 Bereaved Adults
Bibliographic record
Abstract
People bereaved by suicide are at an increased risk of suicide and of dropping out of education or work. Explanations for these associations are unclear, and more research is needed to understand how improving support in educational or work settings for people bereaved by suicide might contribute to reducing suicide risk. Our objective was to explore the impact of suicide on occupational functioning. We conducted a cross-sectional online study of bereaved adults aged 18-40, recruited from staff and students of British higher educational institutions in 2010. We used thematic analysis to analyse free text responses to two questions probing the impact of suicide bereavement on work and education. Our analysis of responses from 460 adults bereaved by suicide identified three main themes: (i) specific aspects of grief that impacted on work performance, cognitive and emotional domains, and social confidence; (ii) structural challenges in work or educational settings including a lack of institutional support, the impact of taking time off, and changes to caring roles; and (iii) new perspectives on the role of work, including determination to achieve. Institutional support should be tailored to take account of the difficulties and experiences described.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".