Parens Patriae: Addressing Maternal and Child Health Disparities in Indigenous Communities in the Philippines
Bibliographic record
Abstract
Maternal and Child Health (MCH) supports a broad array of programs to improve the availability and access to high quality preventive and primary health care for mothers and their children. But despite these policies, IPs/ICCs face a myriad of obstacles when accessing public health systems. The study documents and analyzes the maternal and child health practices of Indigenous Peoples in Sitio Bacao, Palayan City and assesses the impacts of such practices on their health status. This study is anchored on Larker’s Maternal and Child Health Theory (1969), supported by Bowby’s Attachment Theory and Ainsworth’s Child Development Theory (1978). The study used the descriptive and ethnographic methods of research. Primary data was collected through key informant interviews with Aeta mothers, survey questionnaires and field observations. Dietary practices of Aeta mothers and their children’s nutrition were triangulated using secondary data, analysis of Rural Health Unit (RHU) records and survey to the availability and utilization of health services and facilities. Socio-demographic profile of the respondents shows significant relation with their maternal and child health practices such age, education, income and geographical location of the respondents. The study argues that there is much to be desired when it comes to the maternal and child health standard of the Indigenous Peoples of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".