Does social capital increase healthcare financing's projection? Results from the rural household of Uttar Pradesh, India
Bibliographic record
Abstract
In the absence of adequate social security, out-of-pocket health expenditure compels households to adopt coping strategies, such as utilizing savings, selling assets, or acquiring external financial support (EFS) by borrowing with interest. Households' probability of acquiring EFS and its amount (intensity) depends on its social capital - the nature of social relationships and resources embedded within social networks. This study examines the effect of social capital on the probability and intensity of EFS during health events in Uttar Pradesh (UP), India. The analysis used data from a cross-sectional survey of 6218 households, reporting 3066 healthcare events, from two districts of UP. Household heads (HH) reported demographic, socioeconomic, and health-related information, including EFS, for each household member. Self-reported data from Shortened and Adapted Social Capital Assessment Tool in India (SASCAT-I) was used to generate four unique social capital measures (organizational participation, social support, trust, and social cohesion) at HH and community-level, using multilevel confirmatory factor analysis. After descriptive analysis, two-part mixed-effect models were implemented to estimate the probability and intensity of EFS as a function of social capital measures, where multilevel mixed-effects probit regression was used as the first-part and multilevel mixed-effects linear model with log link and gamma distribution as the second-part. Controlling for all covariates, the probability of acquiring EFS significantly increased (p = 0.04) with higher social support of the HH and significantly decreased (p = 0.02) with higher community social cohesion. Conditional to receiving any EFS, higher social trust of the HH resulted in higher intensity of EFS (p = 0.09). Social support and trust may enable households to cope up with financial stress. However, controlling for the other dimensions of social capital, high cohesiveness with the community might restrict a household's access to external resources demonstrating the unintended effect of social capital exerted by formal or informal social control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".