Pengalaman Fisik Dan Psikologis Bidan Desa Puskesmas Cimareme Dalam Menghadapi Pandemi Covid-19
Bibliographic record
Abstract
The increase in COVID-19 cases is a major challenge for health facilities and health workers. Previous research has reported that health workers treating COVID-19 patients experience physical and psychological stress. Village midwives during the pandemic received additional duties in handling and monitoring COVID-19 patients in their working areas. This study aims to explore village midwives' physical and psychological experiences in dealing with their duties and responsibilities during the COVID-19 pandemic. This research uses a qualitative phenomenological approach design. Subject selection technique using a homogenous sampling technique. The subjects in this research consisted of key informants, namely the Village Midwife and triangulation informants, namely the Head of the Community Health Center and the Coordinating Midwife. The determination of research subjects and the number of subjects in this research applies the principles of suitability and adequacy. The results of the research show that the village midwives at the Cimareme Health Center experienced the impact of physical and psychological pressure in dealing with their duties and responsibilities during the COVID-19 pandemic. The physical experiences experienced by the Village Midwife at the Cimareme Health Center are fatigue, lack of sleep, disturbed eating patterns, decreased body weight, decreased immune system, illness and contracting COVID-19. The psychological experiences felt by the village midwives at the Cimareme Health Center are worrying about their own and their family's health, fear of contracting COVID-19, worrying about problems that arise, sad to face death cases, depressed to face cases, and pressured to face the community.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".