Bibliographic record
Abstract
Pandemi covid-19 menciptakan kekhawatiran terhadap beragam kondisi. Berbagai masalah psikis muncul terutama terjadi pada lansia yaitu kecemasan yang berdampak pada penurunan kesehatan, aktivitas fisik, status fungsional, sampai beresiko pada kematian. Tujuan penelitian ini adalah untuk menganalisis kecemasan lansia selama pandemi covid-19 di wilayah Puskesmas Demangan Kota Madiun. Penelitian ini merupakan penelitian kualitatif dengan informan penelitian lansia usia 45-59 tahun yang mengalami kecemasan di wilayah kerja Puskesmas Demangan Kota Madiun. Pengumpulan data menggunakan teknik triangulasi sumber yaitu wawancara, observasi, dan dokumen pada sumber yang sama. Hasil penelitian dilakukan pada 18 lansia diperoleh pernyataan yang menunjukkan lansia mengalami kecemasan selama pandemi covid-19 berjumlah 6 lansia, 12 lansia menganggap covid-19 adalah virus atau penyakit biasa. Lansia yang mengalami kecemasan selama covid-19 dikarenakan memiliki riwayat penyakit degeneratif, sehingga memiliki resiko tinggi terpapar covid-19. Simpulannya adalah kecemasan lansia selama pandemi covid-19 adalah lansia yang memiliki riwayat penyakit degeneratif. Saran yang diberikan adalah lansia tetap mematuhi protokol kesehatan dimanapun berada dan segera melakukan vaksinasi lengkap.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.326 | 0.198 |
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".