PENINGKATAN PENGETAHUAN KEPATUHAN MINUM OBAT PESERTA ILP NOBOREJO MELALUI PENYULUHAN DI WILAYAH KERJA PUSKESMAS CEBONGAN
Bibliographic record
Abstract
Patient compliance in taking medication greatly influences the success of a treatment. Low compliance with taking medication also occurs in elderly patients with a History of Chronic Diseases, so that many patients succumb to the disease and improve the patient’s quality of life. Chronic diseases have caused around 36 million deaths globally. Therefore, people with chronic diseases must control their condition regularly and take medication regularly to maintain the target of optimal disease improvement. The importance of increasing awareness of medication compliance, a community service was held entitled "Improving Knowledge of Medication Compliance for ILP Noborejo Participants Through Counseling in the Cebongan Health Center Work Area". It is hoped that this counseling can increasing public knowledge that medication compliance is important in supporting therapy improvements as long as it is done based on doctor's instructions. The results of this activity show that the average score of respondents in general is at 80%, the average score of the is quite large. Participants have good knowledge of the material presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.033 | 0.004 |
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".