VISITATION SHELTERS FOR THE LONG-TERM CARE SETTING DURING THE PANDEMIC: AN EXPLORATORY STUDY
Bibliographic record
Abstract
Abstract In 2020, the province of Manitoba in Canada devised a plan to provide external visitation shelters for long-term care (LTC) facilities. The intent of the shelters was to alleviate the problems with visitor restrictions due to the COVID-19 pandemic. Dozens of visitation shelters, which were re-purposed shipping containers, were installed at facilities across the province. The purpose of this research was to: examine the experiences of the users through online surveys; make field measurements of the conditions (CO2 values, lighting, acoustics); and conduct an environmental scan of documents (government, media, etc.). Media and government documents provided insights into: design considerations; timing of installation/use; policies and procedures; how much they were utilized; as well as constraints. Survey findings from family/friends (n=20), LTC staff (n=9) and a resident (n=1) revealed that while many agreed that the shelters made a difference for their emotional well-being, many felt that the shelters did not support meaningful connections between residents and visitors. Furthermore, many respondents described the shelters as institutional/sterile and suggested that the décor should be improved to make them homier. Field measurements showed that the ventilation system ensured that CO2 values remained low, indicating that substantial fresh air exchange occurred. Acoustic values indicated that there could be challenges with residents hearing visitors. Lighting values demonstrated that color temperature was appropriate, but lux values were relatively low depending on the specific location in the shelter. Overall, this study was able to outline the advantages and disadvantages to using external visitation shelters in a pandemic situation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".