Abuse of Power on Managing the Health Care Services Policy for the Indonesian National Army Forces and its Implication on Marginalized Soldier’s Health
Bibliographic record
Abstract
This research aims to understand the abuse of power on managing the health service policy and its implication that is very significant in reducing marginalized soldiers’ health due at the ontological level and sociological level. The problem is very interesting to be analyzed by conducting a qualitative research method based on public policy theory, abuse of power theory, and health services theory. Data were collected through in-depth interviews, observation, and documentation related to managing the health service policy cases in Indonesia. Data were analyzed by using interactive models are data reduction, data display, data verification, and supported by triangulation. The results were based on ontological level and sociological level using public policy perspective and power perspective for improving health service policy and practice for The Indonesian Army Forces. Vision and mission of public policy on managing health service policy are needed for providing information to stakeholders related, regarding the regulations and sanctions in health service policy. This result provides inputs for making better regulation on health service policy in Indonesia for state agencies as public officials and practitioners.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".