Caregiving demands and caregivers’ psychological outcomes: the mediating role of perceived injustice
Bibliographic record
Abstract
OBJECTIVES: This study hypothesized that higher caregiving demands are related to higher perceived injustice. Furthermore, this study investigated the mediating role of perceived injustice in the link between caregiving demands and caregivers' psychological well-being. DESIGN: A cross-sectional design. SETTING: The Pain Centre of the university medical centre. SUBJECTS: Participants were 184 family caregivers of patients with chronic musculoskeletal pain. MAIN MEASURES: Participants completed questionnaires that assessed caregiving demands (i.e. The Dutch Objective Burden Inventory), perceived injustice (i.e. The Injustice Experience Questionnaire), how much they considered different sources responsible for the injustice they experienced (i.e. A newly developed inventory), perceived burden (i.e. The Zarit Burden Interview), distress (i.e. The Depression, Anxiety, and Stress Scale), and anger (i.e. The Hostility subscale of the Symptom Checklist-90-Revised). RESULTS: The findings showed that caregiving demands are significantly related to perceived injustice in family caregivers (r = .44; P < .001). Only a small group of family caregivers considered the patient or themselves responsible, but more than half of the caregivers considered healthcare providers at least somewhat responsible for the unjust situation. Finally, perceived injustice mediated the association between caregiving demands and burden (b = .11, CI: .04-.23) and distress (b = .05, CI: .006-.12), but not anger (b = .008, CI: -.01-.06). CONCLUSION: The findings suggest that perceived injustice plays an important role in the well-being of family caregivers and caregivers' well-being may be improved by changing their perceptions about their caregiving tasks and their condition.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".