Bereavement Adjustment and Support among Caregivers
Bibliographic record
Abstract
The objective of this study was to examine the influence of health status, demographics, duration of bereavement, caregiving experience, and the use of formal services on bereavement adjustment for caregivers. Participants were 151 bereaved family caregivers who participated in a telephone survey. The most frequently reported symptoms by caregivers were sleeplessness, followed by depression, and loss of appetite. One hundred thirty-five respondents (89%) felt that things were going reasonably well for themselves at the time of the interview, and 91 respondents (60%) had come to terms with their loved one's death. Hierarchical regression models revealed that being a younger caregiver, reporting poorer mental health status, and being the spouse of the care recipient were predictive of a greater number of reported depressive symptoms in bereavement. Poorer mental health status, being a spousal caregiver, and reporting negative consequences of caregiving on caregiver's health were predictive of poorer recovery in bereavement. Study results also revealed that relatives and friends played an important role in assisting the bereaved to manage the bereavement process. This article identifies factors associated with poor reactions in bereavement and that bereavement as a social process where family and friends play an important role in the recovery process.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".