Bereavement After Informal Caregiving: Assessing Mental Health Burden Using Linked Population Data
Bibliographic record
Abstract
OBJECTIVES: We compared the mental health risk to unpaid caregivers bereaved of a care recipient with the risk to persons otherwise bereaved and to nonbereaved caregivers. METHODS: We linked prescription records for antidepressant and anxiolytic drugs to characteristics and life-event data of members of the Northern Ireland Longitudinal Study (n = 317 264). Using a case-control design, we fitted logistic regression models, stratified by age, to model relative likelihood of mental health problems, using the proxy measures of mental health-related prescription. RESULTS: Both caregivers and bereaved individuals were estimated to be at between 20% and 50% greater risk for mental health problems than noncaregivers in similar circumstances (for bereaved working-age caregivers, odds ratio = 1.41; 95% confidence interval = 1.27, 1.56). For older people, there was no evidence of additional risk to bereaved caregivers, though there was for working-age people. Older people appeared to recover more quickly from caregiver bereavement. CONCLUSIONS: Caregivers were at risk for mental ill health while providing care and after the death of the care recipient. Targeted caregiver support needs to extend beyond the life of the care recipient.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".