Education and Training on Infection Prevention and Control Provided by Long-Term Care Homes to Visitors: A Scoping Review
Bibliographic record
Abstract
Objective: The objective of this study is to identify, examine, and map the literature on infection prevention and control (IPAC) education and training for visitors to long-term care (LTC) homes. Introduction: Visitor restrictions during infectious outbreaks in LTC homes aim to reduce virus transmission to vulnerable residents. The COVID-19 pandemic highlighted the negative impacts of such restrictions, prompting the need for IPAC education for visitors. Inclusion Criteria: This review includes research, narrative papers, and grey literature on IPAC education and training for LTC visitors. It focuses on intentional education aimed at preventing infection transmission. Studies not involving visitors or offered in other settings were excluded. Methods: Following the JBI methodology for scoping reviews, bibliographic databases (CINAHL, Embase, AgeLine, Medline, and ERIC) were searched from 1990 to present in English or French. Data were extracted by two reviewers, focusing on the educational content, delivery mode, frequency, timing, and qualifications of educators. A narrative summary and descriptive statistics were produced. Results: The 26 included documents contained guidelines, policies, educational resources, and opinion papers. Pre-2020, healthcare workers were responsible for educating visitors. Post-2020, more detailed recommendations emerged on the frequency, content, and delivery methods. Key topics included hand hygiene (92.3%), respiratory hygiene (80.8%), and PPE use (73.1%). Conclusions: IPAC education and training for LTC visitors is essential for safe visitation. Future research should evaluate the effectiveness of these educational interventions.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".