Bereavement Training in the Workplace, Can it Help Bridge the Grief Support Gap? A Qualitative Evaluation of Employers’ and Employees’ Views
Bibliographic record
Abstract
Background Bereavement is universal, whilst grief is a natural response to loss, it can have detrimental effects on physical and mental well-being. Bereavement support is not widely available, and workplaces could play a key role in providing consistent, equitable support. Aim We explored the views of employers and employees on bereavement training in the workplace. Methods: St Oswald’s Hospice, UK, delivered bereavement training to 343 employers and employees from 105 regional and national workplaces. Purposive sampling selected 16 individuals for qualitative semi-structured interviews. Findings Findings were organised into four themes: unique challenges faced by employees and employers after bereavement; gap in training; need for tiered, bespoke training; requirement for policy change. Bereavement training in the workplace was seen as beneficial. Conclusions This study addresses a crucial gap in bereavement support by exploring grief training in the workplace. Policy recommendations include: in-house and external support and paid leave as standard.
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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 | 0.037 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".