A Socio-ecological Framing of the Philippine Mental Health Act of 2017
Bibliographic record
Abstract
Filipinos experience numerous barriers to mental health care in their country, such as stigmatization ofillness and behaviours, lack of mental health care services, and resource deficits. The Philippine MentalHealth Act of 2017 was formed to resolve these issues and is in its early stages of implementation.Legislation and policy interventions of this nature are but one level of many interventions that can addresshealth care at a population level. The influence of this legislation for different levels of society is analyzed inorder to understand the different barriers and alternatives to its implementation. Solutions suggested in thelegislation, such as addressing lack of accessibility in rural areas, creating liaisons between different levelsof mental health care, and educating the population regarding mental health, are explored for their effects ondifferent spheres, or levels, of influence. The comprehensiveness of the legislation to address the needs ofmental health service users are highlighted, as are barriers to implementation that inhibit the realization ofpractical strategies. This policy case review and analysis informs program development by highlighting thestrengths and weaknesses aligned to the legislative articles’ target sphere of influence and the population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".