EVALUASI PENATALAKSANAAN PROGRAM PENGELOLAAN PENYAKIT KRONIS (PROLANIS) DI PUSKESMAS KOTA BENGKULU
Bibliographic record
Abstract
Health Chronic Disease Management Program (Prolanis) BPJS Health as a system of health services and proactive approach implemented in an integrated manner involving participants, health facilities and BPJS Health becomes an important factor because it becomes one of the indicators of the fulfillment of the commitment of service at puskesmas. This study aims to determine the management of Prolanis which includes membership, activities, availability of drugs and availability of funds Prolanis, knowing the barriers to the implementation of Prolanis and know the ratio of visits and quality of life of participants Prolanis. The research was conducted by descriptive method through interview technique, check list and WHOQoL questionnaire for quality of life measurement. Data retrieval used primary data by interviewing 40 informants and filling questionnaire on 262 participants of Prolanis. The secondary data was obtained from P-Care Puskesmas application. Analysis of the results of research was using descriptive analysis with narrative exposure accompanied by data tables. The results showed that from 20 Puskesmas 1 Puskesmas had not fulfilled the minimum requirement of Prolanis formation, 4 Puskesmas had not conducted gymnastic and educational activities, 2 Puskesmas did not implement pharmacy service standard for drug distribution Prolanis and 4 Puskesmas had not utilized Prolanis fund provided by BPJS Kesehatan. 50% in some puskesmas cause puskesmas to be in unsafe zone for assessment of capitation indicator. For measurement of quality of life of participants Prolanis obtained 95, 04% of respondents have good quality of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".