Effect of energy shortages on institutional delivery in India
Bibliographic record
Abstract
Abstract Introduction Energy shortages are a common challenge in many low- and middle-income countries and can disrupt the operation of healthcare facilities, which can compromise patient outcomes and affect health service utilization. Maternal healthcare use, in particular, has been found to be negatively correlated with power outages in other contexts. The following study investigates the association between state-level energy shortages and institutional delivery rates in India and how the association varies according to women’s socio-economic status. Methods Using data from the 1998-99 and 2005-06 India Demographic and Health Surveys, along with information on power outages from India’s Central Electricity Authority, we estimate the association between energy shortages and institutional delivery rates using both logistic and multinomial regressions. Results Energy shortages were associated with reduced rates of institutional delivery: a 10% increase in the shortage level corresponded to a 1.1% decline in the percentage of women giving birth in a healthcare facility. Deliveries in public health facilities were more likely to be disrupted by energy shortages than deliveries in private facilities. Conclusion Energy shortages are an important determinant of institutional delivery in India. Given that increasing institutional delivery rates is likely important to reduce maternal mortality, policymakers should work to mitigate the impact of energy disruptions on healthcare seeking behaviours.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".