IMPLEMENTASI KEBIJAKAN PENGELOLAAN LIMBAH MEDIS PADAT RSUD dr. ACHMAD DIPONEGORO PUTUSSIBAU SELAMA PANDEMI COVID-19
Bibliographic record
Abstract
Abstract In providing health community services, hospitals haspotensial to produce solid medical waste. This waste is categorized as hazardous and toxic waste that needs to managed in accordance with the Regulation of the Minister of Environment and Forestry Number P.56/Menlhk-Setjen/2015. At present, it is known that the implementation of solid medical waste management policies in RSUD dr. AchmadDiponegoroPutussibau is still having problems. This condition is exacerbated by the increase in solid medical waste during the Covid-19 pandemic. This research aims to analyze the process of implementing solid waste management policies policies in RSUD dr. AchmadDiponegoroPutussibauduring the Covid-19 pandemic and the obstacles faced in implementing the policy. The study used a case study with qualitative approach. While the policy implementation model is based on the Edward III approach, the results are analyzed by qualitative descriptive. The results showed that the process of implementing solid medical waste management policies in RSUD dr. AchmadDiponegoroPutussibau during the Covid-19 pandemic is still not optimal. There are 4 factors that influence the process of implementing then policy, including communication, resources, dispositions, and bureaucratic structure. Meanwhile, the obstacles that hinder the process of implementing the policy are limited human resources, limited budget, lack of awareness and understanding, and lack of coordination. The efforts that can be made so that the optimal implementation process is to improve the aspect of resources through increasing human resources and budgets, increasing awareness and understanding of officers and estabilishing good coordination with relevant agencies. Keywords : implementation, policy, AchmadDiponegoroPutussibau, solid medical waste, Covid-19
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".