Good Hospital Governance at the Indonesian Hospital
Bibliographic record
Abstract
This study aims to describe the commitment of stakeholders in implementing the Good Hospital Governance policy at the Undata Regional General Hospital, Central Sulawesi Province, Indonesia. The method used is a qualitative exploratory approach with 13 (thirteen) informants who were determined by purposive sampling, the data analysis used was an interactive model data analysis from Miles and Huberman by triangulating methods and data sources. The results showed that the successful implementation of the Good Hospital Governance policy at Undata Hospital, Central Sulawesi Province which was viewed from 6 (six) supporting aspects of the implementation of the Van Metter and Van Horn policies had not been running properly. That is; aspects of resources, aspects of the characteristics of the executing agent, aspects of the attitudes/tendencies (dispositions) of the executing agents, and aspects of the external environment (economic, social, and political). Besides, an implementation must also be supported by the commitment of the owner, board of directors, and management as well as all hospital staff, to implement the principles of transparency, accountability, independence, responsibility, equality, and fairness.
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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 | 0.003 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".