Working informal caregivers and risk of long-term sickness absence and unemployment: a Danish nationwide cohort study of effect modification by psychosocial working conditions
Bibliographic record
Abstract
Abstract Aim Balancing work and informal-care responsibilities is burdensome. We examine the risk of long-term sickness absence (LTSA) and unemployment among working caregivers of children with versus without mental disorders. We tested modification by occupational emotional demands and influence. Subject and methods This register-based matched cohort-study used LTSA and unemployment data on 1,927,098 Danish caregiver-child pairs, tracking incidence of child mental disorders (2000–2018). Exposure to occupational emotional demands and influence at baseline were assigned by job-exposure matrices. Mental disorders were indicated by psychiatric hospital contact, drug use or treatment for substance abuse. Caregivers of children with and without mental disorders were matched 1:5 by age, sex (child/caregiver), occupation, working hours (caregiver), and baseline calendar-week. Cox models estimated hazard ratios (HR) of LTSA (≥ 4 weeks) or unemployment with 10-year follow-up. LTSA and unemployment were analyzed separately, stratified by caregiver sex. Analyses were adjusted for socio-demographics. We tested effect modification and estimated relative excess risk due to interaction (RERI). Results Caregivers of children with any mental disorder had higher risk of LTSA (females: HR = 1.96, 95% CI = 1.94;1.98, males: HR = 1.63, 95% CI = 1.60;1.65) and unemployment (females: HR = 1.14, 95% CI = 1.12;1.15, males: HR = 1.14, 95% CI = 1.12;1.16). The RERI for LTSA was elevated among caregivers exposed to high emotional demands (females: 0.34, 95% CI = 0.28;0.40, males: 0.06, 95% CI < 0.01;0.12) or low influence (females: HR = 0.19, 95% CI = 0.14;0.24, males:HR = 0.12, 95% CI = 0.07;0.18). Conclusion Working caregivers of children with mental disorders have increased risk of LTSA and unemployment. Societal and workplace initiatives, e.g., targeting working conditions, may help balance informal-care and work responsibilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".