Family Caregiver Mental Health: Linking Family Care Regime, Intersectionality, and Stress Process Frameworks
Bibliographic record
Abstract
Abstract Although the implications of family care regime, social location, and stress process factors for the mental health of family caregivers have been well-documented individually, there is a lack of research that integrates these factors. Yet, linking family care regime and intersectionality approaches to stress process theorizing provides us with one possible explanation of the mechanisms potentially linking family care regime and intersecting structural inequalities to mental health outcomes. This paper draws on pooled data from the 2012 and 2016 European Quality of Life Surveys (EQLS - N=6,007) to assess direct and indirect associations between family care regime and the self-reported mental health (SRMH) of family caregivers, together with the additive and interactive associations involving social location (gender, age, socio-economic status, and marital status), and stress process factors (stressors and resources). The results of a series of weighted least squares regression analyses reveal that family care regime has a direct association with SRMH and that social location and stress process factors partially mediate this association. Additionally, the results suggest that additive and interactive social location factors have direct associations with SRMH and that stress process factors also partially mediate the association. Lastly, stress process factors are associated with SRMH as expected. Overall, our findings provide initial support for the value of linking family care regime, intersectionality and stress process frameworks for an understanding of the mental health implications of family caregiving.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| 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".