Uji Coba Model Pendeteksian Terhadap Penganiayaan Usia Lanjut di Keluarga
Bibliographic record
Abstract
AbstrakTujuan penelitian ini adalah tersusunnya model pendeteksian terhadap penganiayaan usia lanjut di keluarga. Desain yang digunakan dalam penelitian ini adalah riset operasional, yang pelaksanaannya pada tahap I desain yang digunakan adalah eksploratif, pada tahap II menggunakan desain konfirmatif dan tahap III pengambilan data untuk mengidentifikasi masalah penganiayaan usia lanjut di keluarga. Sampel yang digunakan 11 petugas kesehatan dan 44 usia lanjut yang tinggal di keluarga yang dipilih secara acak di Kecamatan X di wilayah Jakarta Timur. Berdasarkan analisis data diperoleh hasil: instrumen yang digunakan pada penelitian ini didapatkan nilai kevalidan sebesar 0,6900 – 0,7378 yang merupakan nilai lebih dari nilai r tabel (r = 0,288), ini menyatakan bahwa hubungan antar pertanyaan dengan nilai keseluruhan instrument cukup baik, dengan realibilitas (menggunakan α-cronbah) sebesar 0,7275. Masalah penganiayaan yang ditemukan adalah sebesar 18,18% usia lanjut di keluarga mengalami penganiayaan fisik, 97,73% penganiayaan emosi, 13,64% penganiayaan seksual, 31,82% penganiayaan ekonomi/finansial, 61,36% pengabaian dan 29,55% mengalami penelantaran. Usia lanjut yang sering mengalami penganiayaan yaitu yang berumur 60 – 75 tahun, berjenis kelamin perempuan, bersuku bangsa Jawa, Agama Islam, tingkat pendidikan SD dan sudah berstatus janda/duda. Model ini dapat digunakan pendeteksian terhadap penganiayaan usia lanjut secara dini sehingga bagi usia lanjut yang sudah terdeteksi dianiaya oleh keluarga dapat dilakukan penanganan secepatnya. AbstractThe purpose of the study was to arrange detection model of elderly abuse in family. This study used operational research that first stage use eksploratif, second stage use confirmative design, and third stage was data collection to identify elderly abuse in family. Sample that was taken are 11 health staff and 44 elderlies who live in family and randomly chosen. The area of this study was in X district, East Jakarta. The result of this study indicated that validity value is 0,6900-0,7378 (r table = 0,288) with reability (use -cronbah) is 0,7275. This study showed that 18,18% of elderly have physical abuse, 97,73% have emotional abuse, 13,64% have sexual abuse, 31,82% have economic/ financial abuse, 61,36% neglect and 29,55% abandon. The elderly that tend to be abused ranges about 60-75 years old, women, Javanese, Islamic, level of education background is primary school, widow/widower. This study can be used to detect elderly abuse as early as possible to improve elderly welfare in community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".