Peer Group Support untuk Menurunkan Tingkat Depresi pada Lansia di UPT PSLU Blitar
Bibliographic record
Abstract
Indonesia has entered an era where the population structure is elderly and estimated that in 2020 the number of elderly reach 28.8 million (11.34%) peoples with a life expectancy of 71.1 years old. As people getting old, aging and physical changes are unavoidable. This changes can lead to mental disorders. Depression is one of the many common mental disorders in the elderly due to aging. Based on data in Canada, 5-10% of elderly living in the community are depressed, while those living in the institutional environment of 30-40% have depression and anxiety. One effort that can be done to deal with depression in the elderly is to use intervention Peer Group Support. Methods: This research used Pre-Experiment with the one group pre-post test design. The total sample was 30 respondents taken by purposive sampling. The data were analyzed by Paired T Test,with significance value of 0.05. Results:based on test result of the paired t test, there was differences in level of depression before and after peer group support (p=0,001). Discussion:with the provision of peer group support interventions,it could reduce the level of depression in the elderly at UPT PSLU Blitar.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".