Guidance to (Re)integrate Caregivers as Essential Care Partners Into the LTC Setting: A Rapid Review
Bibliographic record
Abstract
OBJECTIVES: This rapid review aimed to identify the strategies used to (re)integrate essential caregivers (ECs) into the LTC setting, particularly pertaining to principles of equity, diversity, and inclusion. In addition, this rapid review aimed to identify the strategies used during prior infectious disease threats, when similar blanket visitor restrictions were implemented in LTC homes. The review was part of a larger effort to support LTC homes in Ontario. DESIGN: A rapid review was conducted in accordance with principles from the Canadian National Collaborating Centre for Methods and Tools. SETTING AND PARTICIPANTS: ECs, residents, staff, and policy decision makers in long-term care home settings. METHODS: Five electronic databases were searched for academic and gray literature using predefined search terms. Selected documents met inclusion criteria if they included policy guidance or an intervention to (re)integrate ECs into LTC homes at the local, national, and/or international level. RESULTS: In total, 15 documents met the inclusion and exclusion criteria. All documents retrieved focused on the context of COVID-19. Documents were either policy guidance (n = 13) or primary research studies (n = 2). Documents differed in these notable ways: Definition of EC; the degree to which an EC is recognized for her or his role in the care of the resident; the degree to which ECs are (re)integrated into the LTC setting is prioritized; response to community spread of COVID-19; visitation during an outbreak or if a resident is symptomatic; the reliance on equity, diversity, and inclusion principles; and lastly, monitoring and improving the process. CONCLUSIONS AND IMPLICATIONS: Using an equity, diversity, and inclusion lens, we posit promising practices for (re)integration. It is clear from the rapid review that more research is needed to understand the efficacy of policies and guidelines to (re)integrate ECs into the LTC setting. Until such evidence is available, expert opinion will drive best care practices.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".