Expert Needs of Healthy Public Health Centre Development in the Archipelago Area of South Sulawesi
Bibliographic record
Abstract
This research aimed to develop the indicators of Healthy Public Health Centers in the archipelago region of South Sulawesi. This research was conducted by applying qualitative method through a literature review approach related to the construction of indicators for the model of a Healthy Public Health Center in the archipelago region. The informants involved in this study were from Health Department, Public Health Center, Social Service, Environment Service, Tourism Office, Fisheries and Marine Service, the Head of Sub- District, Sub-Village/lurah, Healthy Regency/City Forum, Healthy Sub-District Communication Forum, Working Groups at the village level, NGOs, public figure, religious leaders, academics, and other informants. This research also applied FGD and In-depth Interview. The result of this study is the discovery of dimensions consisting of indicators that make up the model of a Healthy Public Health Center in the archipelago region. The dimensions consisted of (1) Location, (2) Access, (3) Basic service program, (4) a specific program (innovation) (5) Human Resources (HR), (6) Community Empowerment, (7) Public Health Center Working Group (Pokja PKM Sehat) and (8) Archipelago Healthy Public Health Center. This study found a number of indicators of Healthy Health Centers that can be applied specifically to archipelagic areas.
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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 | 0.004 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".