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Record W4382047216 · doi:10.33024/jkpm.v6i7.10108

Peningkatan Kapasitas Kader Kesehatan dalam Upaya Skrining dan Pencegahan Penyakit Kronis di Wilayah Kabupaten Bandung

2023· article· id· W4382047216 on OpenAlexaff
Titis Kurniawan, Hasniatisari Harun, Nursiswati Nursiswati

Bibliographic record

VenueJURNAL KREATIVITAS PENGABDIAN KEPADA MASYARAKAT (PKM) · 2023
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

ABSTRAK Penyakit kronik adalah masalah kesehatan global yang serius, termasuk di Indonesia. Upaya pencegahan dengan optimalisasi peran kader di Posbindu dalam skrining merupakan strategi penting dalam pencegahan penyakit kronik. Meski demikian, dalam tataran praktik peran kader kesehatan lebih berorientasi pengelolaan dan belum mencakup program pencegahan. Program Pengabdian pada Masyarakat ini ditujukan untuk meningkatkan kapasitas kader Posbindu dalam peranannya menjalankan program skrining penyakit kronik di masyarakat. Kegiatan dijalankan di Desa Banjaran, Kec. Banjaran, Kab. Bandung. Program PPM dimulai penyampaian materi terkait penyakit kronis, materi skrining tingkat risiko penyakit kronis dan pencegahannya serta materi terkait peran kader. Pengetahuan kader dicek menggunakan kuesioner pre-test dan post-test. Sebanyak 88 kader Kesehatan terlibat dalam kegiatan. Pengetahuan kader terkait penyakit kronik, faktor risiko dan pencegahanya menunjukkan adanya peningkatan sebesar 15% (rerata jawaban benar pre-test 6,03 vs. post-test 7,5). Kegiatan PPM ini efektif meningkatkan pengetahuan dan kapasitas kader kesehatan di Wilayah Banjaran dalam pengenalan dan skrining tingkat risiko penyakit kronik. Selain itu, penting bagi kader menerapkan program skrining di Posbindu sebagai upaya pengenalan dan monitoring faktor risiko penyakit kronik baik yang ada pada dirinya, peserta Posbindu, maupun masyarakat luas. Kata Kunci: Penyakit Kronik, Kader, Posbindu, Skrining, Upgrading ABSTRACT Chronic disease is a serious global health problem, including in Indonesia. Prevention efforts by optimizing the role of Posbindu cadres for screening and early detection are important strategies in preventing chronic diseases. However, at the practical level, the role of health cadres is more management oriented and does not include prevention programs. This community service program aimed to improve health cadres’ capacity for screening and early detection of chronic diseases in programs the community. It was conducted in Banjaran Village, Banjaran Sub-District, Bandung regency. Apart from participating in Posbindu activities, the team also conducted upgrading program covering health education on chronic disease, its’ prevention and the role of cadres. Knowledge of cadres was checked using pre and post-test questionnaires. A total of 88 health cadres in the area were involved in an upgrading activity. The knowledge of cadres regarding chronic diseases, risk factors and their prevention showed an increase of 15% (mean pre-test correct answers 6, 03 vs. post-test 7.5). This program effectively improves the health cadres’ knowledge and capacity in recognizing and detecting the risk level of chronic disease, particularly DMT2. Additionally, it is important for the cadres to implement screening and early detection programs at Posbindu as an effort to identify and monitor risk factors for chronic disease both in themselves, Posbindu participants, and the wider community. Keywords: Chronic Disease, Cadres, Posbindu, Screening, Upgradin

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0420.007

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.

Opus teacher head0.039
GPT teacher head0.313
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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