Predictors of Caregiver Burden Among Carers of Suicide Attempt Survivors
Bibliographic record
Abstract
Abstract. Background: Family members often provide informal care following a suicide attempt. Carers may be vulnerable to caregiver burden. Yet, little is known about what contributes to this. Aims: To determine the predictors of caregiver burden in those carers who support people who have attempted suicide. Method: An online survey of 435 participants assessed exposure to suicide, caring behaviors, and psychological variables and caregiver burden. Results: A multivariate model explained 52% of variance in caregiver burden. Being female, closeness to the person, impact of suicide attempt, frequency of contact pre-attempt, and psychological distress were positively associated with caregiver burden. Confidence in supporting the person after suicide attempt, perceived adequacy of healthcare the person received and the support the carer received, and suicidal ideation of the carer were negatively associated with caregiver burden. Moderation analysis suggested that carers with high levels of distress reported negative association between suicidal ideation and caregiver burden. Limitations: The cross-sectional online survey design of self-identified carers is a limitation of the study. Conclusion: Carers are highly distressed, and if unsupported report increased suicide ideation. In their caring roles they may have contact with support services, thus attending to their needs may ameliorate caregiver burden and associated negative 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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".