Bereavement and mental health factors associated with seeking and receiving support following loss among Canadian bereaved adults
Bibliographic record
Abstract
PURPOSE: The death of a loved one is one of life's most ubiquitous events that can increase risk of mental health difficulties. Bereavement support is one of the few factors influencing grief-related mental health outcomes that can be modified after bereavement. This study sought to determine the proportion of bereaved people that want and receive support from different sources following a bereavement, and the bereavement and mental health-related factors associated with wanting and receiving bereavement support. METHODS: Data was derived from a cross-sectional survey of bereaved adults (n = 1170) living in Ontario, Canada. RESULTS: Over a third of the sample (38.9%; n = 455) reported wanting support in coping with their loss. These individuals exhibited distinct loss-related characteristics and reported higher levels of anxiety, depression, and prolonged grief symptoms. Most of these individuals received support and a small number of participants who didn't want support received it regardless. The most common sources of support were family members, friends, and other bereaved individuals, and these sources were generally found to be helpful. Those who accessed multiple types of support were those with the highest levels of anxiety, depression, and prolonged grief symptoms. CONCLUSION: Most individuals wanting support after a loss can access it and find it beneficial. The desire for support is closely tied to psychological distress, highlighting the need to prioritize formal support for those in distress and rely on existing social networks for those who are not. Such an approach embodies an 'assets-based' bereavement model, enhancing community capacity for effective support provision.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".