With COVID Comes Complexity: Assessing the Implementation of Family Visitation Programs in Long-Term Care
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Coronavirus disease 2019 (COVID-19) pandemic visitor restrictions to long-term care facilities have demonstrated that eliminating opportunities for family-resident contact has devastating consequences for residents' quality of life. Our study aimed to understand how public health directives to support family visitations during the pandemic were navigated, managed, and implemented by staff. RESEARCH DESIGN AND METHODS: Guided by the Consolidated Framework for Implementation Research, we conducted video/telephone interviews with 54 direct care and implementation staff in six long-term care homes in two Canadian provinces to assess implementation barriers and facilitators of visitation programs. Equity and inclusion issues were examined in the program's implementation. RESULTS: Despite similar public health directives, implementation varied by facility, largely influenced by the existing culture and processes of the facility and the staff understanding of the program; differences resulted in how designated family members were chosen and restrictions around visitations (e.g., scheduling and location). Facilitators of implementation were good communication networks, leadership, and intentional planning to develop the visitor designation processes. However, the lack of consultation with direct care staff led to logistical challenges around visitation and ignited conflict around visitation rules and procedures. DISCUSSION AND IMPLICATIONS: Insights into the complexities of implementing family visitation programs during a pandemic are discussed, and opportunities for improvement are identified. Our results reveal the importance of proactively including direct care staff and family in planning for future outbreaks.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".