Determinan Partisipasi Masyarakat terhadap Program Posbindu PTM: Evaluasi Program di Wilayah Kerja Puskesmas
Bibliographic record
Abstract
Tujuan kegiatan adalah mengetahui evaluasi terhadap program posbindu PTM di wilayah kerja puskesmas Datuk Bandar Kota Tanjungbalai. Evaluasi intervensi ini menggunakan metode kuantitatif, yaitu untuk menambahkan pengetahuan menggunakan data angka untuk menemukan keterangan data. Populasi penelitian ini 104 orang dan sampel 104 responden yaitu lansia, orang dewasa dan remaja berusia 15 hingga 59 tahun. Metode pengumpulan data yaitu menggunakan data primer dan sekunder. Teknik pengambilan sampel menggunakan Probability sampling, yaitu teknik yang memberikan peluang yang sama bagi setiap anggota populasi dan menggunakan analisis Uji Chi Square. Hasil intervensi ini adalah pengetahuan, sikap, dukungan keluarga, dukungan kader, dukungan tenaga kesehatan terdapat pengaruh yang signifikan karena memiliki nilai p-value < 0,05. Pengetahuan memiliki nilai (p-value 0,000), sikap (p-value 0,001), dukungan kader (p-value 0,009), dukungan keluarga (p-value 0,000), dan dukungan tenaga kesehatan (p-value 0,030). Disimpulkan pengetahuan, sikap, dukungan kader, dukungan keluarga, dan dukungan tenaga kesehatan ada pengaruh yang signifikan dengan determinan partisipasi masyarakat terhadap program Posbindu PTM di Wilayah Kerja Puskesmas Datuk Bandar Kota Tanjungbalai.Kata kunci: Determinan; evaluasi; partisipasi; program Posbindu PTM.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 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".