Strategies to improve family visitation during pandemics or outbreaks in acute care
Bibliographic record
Abstract
Background: During the Coronavirus Disease 2019 (COVID-19) pandemic, hospitals were required to allow visitation while also adopting more effective approaches to managing patient care and family support. Balancing infection control practises with the emotional and psychological needs of patients and their families is essential. This study explores approaches to enhance visitation practices, thereby contributing to better patient outcomes and overall well-being. Methods: A purposive sampling method was used to select 14 participants from the circle of care (COC) at an acute care institution in Western Ontario, Canada. The COC consisted of the hospital Chief Patient Experience Officer, Access and Flow leaders, frontline nurses, infection control practitioners, occupational health professionals, clinical service managers, and resource nurses. A team mapping method was used to engage the COC in gathering information. The team mapping method consisted of three stages: a development phase, a team mapping phase, and an integration phase. The collected responses were coded, and thematic analysis was used to identify themes. Results: The analysis yielded four key themes: (1) fostering interpersonal relationships and communication among the COC, (2) developing new approaches to patient admission and care, (3) modifying the physical layout of hospital spaces, and (4) raising public and community awareness to empower individuals to make well-informed choices. Conclusion: Any change to a hospital’s visitation policy should include input from patients, families, clinicians, and hospital subject matter experts. Allowing visitation access, especially during a pandemic, may improve the mental and emotional well-being of patients and families and may also provide a sense of normalcy despite hospitalization.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".