Faktor-faktor yang Berhubungan dengan Perilaku Kunjungan Lansia ke Posyandu Lansia di Kerja Puskesmas Kampar Kabupaten Kampar Tahun 201
Bibliographic record
Abstract
For the number of visits to the elderly elderly IHC from year to year is low, as it is still below the target minimum service standards (MSS) which is 70%. This study aims at knowing the factors related to the behavior of the elderly to visit the elderly posyandu Knowledge, Attitude, Family Support, The Role of Health Personnel, Access, Employment Status, Education, Gender, Age. Type of research is the study used cross-Analytical (Analytical Cross Sectional Study) with a sample of 250 people. The sampling process is done by proportional random sampling, data analysis was performed using univariate, bivariate, and multivariate logistic regression Test Doubles. Results of this study was the proportion of elderly who visited posyandu 102 people (40.8%), and 148 people who did not visit (59.2%). While the variable knowledge gained POR 8.2 (95% CI: 4.3 to 15.7), attitude POR 2.1 (95% CI: 1.16 to 3.9), family support POR 2.4 (1, 27 to 4.64), the role of health workers POR 2.6 (95% CI: 1.36 to 5.32), access POR 2.09 (95% CI: 1.12 to 3.9), education POR 2 , 3 (95% CI: 1.1 to 4.86). The conclusion of this study is that there is a relationship between the Knowledge, Attitude, Family Support, The Role of Health Personnel, Access, Education to conduct neighborhood health center visits to elderly seniors. Advice for health promotion officer needs to be planned, directed, and sustained and the officer immediately diposyandu varying the existing activities, and the family must always provide support and are available to take seniors keposyandu
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".