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Record W3196176049 · doi:10.1016/j.ssmph.2021.100901

Does social capital increase healthcare financing's projection? Results from the rural household of Uttar Pradesh, India

2021· article· en· W3196176049 on OpenAlexaff
Md Zabir Hasan, William T. Story, David Bishai, Akshay Ahuja, Krishna D. Rao, Shivam Gupta

Bibliographic record

VenueSSM - Population Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
FundersJohns Hopkins University
KeywordsSocial capitalProbit modelSocial securitySocial supportSocioeconomic statusMultilevel modelDemographic economicsDescriptive statisticsBusinessSocioeconomicsEconomicsPsychologyDemographyEconometricsSociologyStatisticsSocial psychologyPopulation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.351
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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