Factors associated with higher levels of grief and support needs among people bereaved during the pandemic: Results from a national online survey
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has affected millions of people’s experiences of bereavement. We aimed to identify risk factors for grief and support needs. Methods Online survey of people bereaved in the UK (deaths 16 March 2020-2 January 2021), recruited via media, social media, national associations/organisations. Grief was assessed using the Adult Attitude to Grief (AAG) scale, which calculates an overall index of vulnerability (IOV) (range 0-36). Practical and emotional support needs were assessed in 13 domains. Results 711 participants, mean age 49.5 (SD 12.9, range 18-90). 628 (88.6%) were female. Mean age of the deceased 72.2 (SD 16.1). 311 (43.8%) deaths were from confirmed/suspected COVID-19. Mean IOV was 20.41 (95% CI = 20.06 to 20.77). 28.2% exhibited severe vulnerability (IOV ≥ 24). In six support domains relating to psycho-emotional support, 50% to 60% of respondents reported high/fairly high levels of need. Grief and support needs increased strongly for close relationships with the deceased (versus more distant) and with reported social isolation and loneliness ( P < 0.001), whereas they reduced with age of the deceased above 40 to 50. Other risk factors were place of death and reduced support from health professionals after death ( P < 0.05). Conclusions High overall levels of vulnerability in grief and support needs were observed. Relationship with the deceased, age of the deceased, and social isolation and loneliness are potential indicators of those at risk of even higher vulnerability in grief and support needs. Healthcare professional support after death is associated with more positive bereavement outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".