Access to affordable daycare and women’s mental health in Rajasthan, India: Evidence from a cluster-randomised social intervention
Bibliographic record
Abstract
Background: Women in India are often responsible for unpaid household work, family caregiving, and paid work, which can contribute to poorer mental health. The provision of childcare has the potential to improve women's mental health, but evidence on the effects of providing access to daycare is limited. Methods: We designed a cluster-randomised trial and used data from a sample of 2858 mothers with age-eligible children from 160 village hamlets in rural Rajasthan, India, to evaluate the impact of providing access to a community-based daycare programme on social and emotional aspects of women's mental health. We conducted a baseline survey in early 2016, randomised hamlets to intervention or control groups approximately six months later, and delivered the final post-intervention survey approximately two years thereafter. Results: Treatment assignment increased the probability that a respondent used a daycare over the two-year follow-up by 40.9 percentage points. Providing randomised access to a daycare resulted in 0.2 (95% confidence interval (CI) = -0.1, 0.4) fewer symptoms of mental distress, representing a 9.5% decline compared to the baseline mean of 2.1 symptoms, as well as a 3.7 (95% CI = -0.8, 8.3) percentage point increase in the proportion of women who reported feeling very happy, equivalent to an 11.0% increase relative to the baseline mean of 33.6%. Among social indicators, treatment assignment was associated with a 5.6 (95% CI = -1.2, 12.4) percentage point increase in membership in an association, a relative increase of 43.4% compared to the baseline mean of 12.9%. The intervention did not have an appreciable impact on measures of life satisfaction or trust in institutions. Two-stage least squares instrumental variable analyses showed that daycare use decreased mental distress by 0.4 (95% CI = -0.1, 0.8) symptoms, increased the proportion of women who were very happy by 9.4 (95% CI = 0.0, 17.6) percentage points, and increased membership in an organisation by 15.9 (95% CI = 8.4, 23.7) percentage points. Conclusions: The provision of affordable, community-based daycare was associated with substantial uptake and showed potential for improving mothers' mental health in a rural context where most women were not employed in the formal labour force. Registration: ISRCTN clinical trial registry (ISRCTN45369145), registered on 16 May 2016; American Economic Association's registry for randomised controlled trials (AEARCTR-0000774), registered on 15 July 2015.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".