Visiting Policies of Hospice Wards during the COVID-19 Pandemic: An Environmental Scan in Taiwan
Bibliographic record
Abstract
During an epidemic, almost all healthcare facilities restrict the visiting of patients to prevent disease transmission. For hospices with terminally ill patients, the trade-off between compassion and infection control becomes a difficult decision. This study aimed to survey the changes in visiting policy for all 76 hospice wards in Taiwan during the COVID-19 pandemic in March 2020. The altered visiting policies were assessed by the number of visitors per patient allowed at one time, the daily number of visiting slots, the number of hours open daily, and requisites for hospice ward entry. The differences in visiting policies between hospice wards and ordinary wards were also investigated. Data were collected by reviewing the official website of each hospital and were supplemented by phone calls in cases where no information was posted on the website. One quarter (n = 20) of hospice wards had different visiting policies to those of ordinary wards in the same hospital. Only one hospice ward operated an open policy, and in contrast, nine (11.8%) stopped visits entirely. Among the 67 hospice wards that allowed visiting, at most, two visitors at one time per patient were allowed in 46 (68.6%), one visiting time daily was allowed in 32 (47.8%), one hour of visiting per day was allowed in 29 (43.3%), and checking of identity and travel history was carried out in 12 wards (17.9%). During the COVID-19 pandemic, nearly all hospice wards in Taiwan changed their visiting policies, but the degree of restriction varied. Further studies could measure the impacts of visiting policy changes on patients and healthcare professionals.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".