Factors that affect social health insurance enrollment and retention of the informal sector in the Philippines: a qualitative study
Bibliographic record
Abstract
Abstract Background. The primary goal of providing social protection to informal sector workers is to guarantee a minimum level of income and dignity that allows for better protection against income shocks and other vulnerabilities. With the passage of the Universal Health Care Act in the Philippines, the determination of factors affecting enrollment and retention into social health insurance among informal sector workers in the Philippines is crucial to design appropriate policies and programs fit to their needs. Methods. This study aimed to identify factors that affect social health insurance enrollment and retention of the informal sector in the Philippines through qualitative research methods of face-to-face, semi-structured focus group discussion and key informant interviews. Results. The analysis identified five broad themes that affect informal sector enrollment and retention in social health insurance: 1) overlaps in categorization, 2) insufficient or inappropriate social health insurance initiatives for the informal sector, 3) awareness and understanding of social health insurance, 4) supply side factors, and 5) convenience and amount of premium payment. Conclusion. Informal workers are individuals who are not covered by protective labor laws and tend to not belong or contribute to a national health insurance scheme. In the case of the Philippines, the diversity of informal work and dynamic nature of the sector works against an ideal one-size-fits-all solution to increasing informal sector enrollment and retention to social health insurance.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 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) | 0.004 | 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".