Nurse Practitioners in LTC can Mitigate the Harmful Effects of Social Isolation
Bibliographic record
Abstract
Abstract Aim: The aim of this narrative literature review is to summarize the current literature surrounding social isolation in long term care (LTC) during the COVID-19 pandemic, and to highlight the role of nurse practitioners in addressing social isolation. Background: LTC homes in Ontario struggled with protecting their residents from the COVID-19 virus and enforcing lockdowns which including restricting outside visitors. Many LTC homes have nurse practitioners (NPs) available to support implementation of public health policies, while also providing medical oversight to the home. Despite having funding policies in Ontario for NPs in LTC, many homes do not have one as part of their health care team. Methods: 15 peer-reviewed articles from 2019-2022 are included in this review, focusing on articles to assist in exploring the research question ‘What was the role of nurse practitioners in addressing factors associated with social isolation in LTC during the COVID-19 pandemic’? Findings: Social isolation was identified as being a concern pre-pandemic, and was intensified during the pandemic due to lockdown measures and visitor restrictions. Nurse practitioners are well positioned to identify risk for isolation, and create plans to mitigate the effects for LTC residents. Conclusion: Future outbreaks and/or pandemics will hold the same requirement for lockdown, but an assessment tool to predict exposure risk to the home and to individuals would allow for purposeful implementation of appropriate levels of isolation. Having nurse practitioners in all LTC homes would be an effective and appropriate way to monitor and implement such protocols, and to mitigate potentially harmful outcomes of social isolation.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".