The Extemporaneous Compounding at Primary Health Care Centers: Characteristic and Personnel
Bibliographic record
Abstract
OBJECTIVE: Extemporaneous compounding is still done for clinical practices in several countries, one of them is in Indonesia. This is due to unavailability or limited formula for pediatric patients or limited budget in the procurement of medicines. This study was aimed at recognizing the compounding personnel’s characteristics and the characteristics of extemporaneous compounding based on prescriptions at primary health care centers.METHOD: This study used cross sectional survey method conducted at 24 Primary Health Care Centers in Banyumas Regency, Central Java, Indonesia. Retrospective data collection was conducted on 1200 prescription sheets in the period between April to June 2017.RESULT: Compounding personnel at Primary Health Care Centers in Banyumas Regency, Central Java, Indonesia consist of 59 personnel consisting of pharmacist (40.68%), pharmaceutical technicians (10.17%), compounding personnel having health educational background (22.03%) and those from non-health educational background (27.12%). Most of the compounding personnel are women (74.58%) while the rest of 25.42% is men. Mostly, compounding personnel working at the Primary Health Care Centers (35.60%) are in their productive age; they are between 20 to 30 years old. Most of their educational background are undergraduates (S1); they are 27 persons (45.76%). Commonly, the prescribers were general practitioners (79.75%), midwives (15.08%), dentists (2%), and nurses (3.17%). Children (97.67%) are the patients who are mostly prescribed at Primary Health Care Centers. The pediatric patients were dominated by 0–5 years-old children (74.33%) while adult patients were only 2.33%. The most common prescription is one extemporaneous compounding per prescription sheet as much as 97.58%. The form of extemporaneous compounding is mostly 96.42% oral; it consisted of 88.36% puyer (crushed tablet) and 8.06% suspension.CONCLUSIONS: In this study, drug compounders in Primary Health Care Centers are mostly pharmacists. The prescribers of extemporaneous compounding in Primary Health Care Centers are mostly general practitioners, some midwives and nurses also prescribe. Patients receiving the prescription are dominated by pediatric patients range between 0–5 years old. It is common to find only one extemporaneous compounding per prescription sheet, with puyer being the most prevalent.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".