Exploring the Role of Social Networks in Facilitating Health Service Access Among Low-Income Women in the Philippines: A Qualitative Study
Bibliographic record
Abstract
Despite efforts to implement universal health care coverage (UHC) in the Philippines, income poor households continue to face barriers to health care access and use. In light of recent UHC legislation, the aim of this study was to explore how gender and social networks shape health care access and use among women experiencing poverty in Negros Occidental, Philippines. Semi-structured interviews were conducted with women (n = 35) and health care providers (n = 15). Descriptive statistical analyses were performed to report demographic information. Interview data were analyzed thematically using a hybrid deductive-inductive approach and guided by the Patient-Centred Access to Health Care framework. Women's decisions regarding health care access were influenced by their perceptions of illness severity, their trust in health care facilities, and their available financial resources. Experiences of health care use were shaped by interactions with health professionals, resource availability at facilities, health care costs, and health insurance acquisition. Women drew upon social networks throughout their lifespan for social and financial support to facilitate healthcare access and use. These findings indicate that social networks may be an important complement to formal supports (eg, UHC) in improving access to health care for women experiencing poverty in the Philippines.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".