Promising best practices implemented in long-term care homes during COVID-19 pandemic to address social isolation and loneliness: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Prior to the COVID-19 pandemic, social isolation and loneliness (SIL) affected at least one-third of the older people. The pandemic has prompted governments around the world to implement some extreme measures such as banning public gatherings, imposing social distancing, mobility restrictions and quarantine to control the spread and impact of the novel coronavirus. Though these unprecedented measures may be crucial from a public health perspective, they also have the potential to further exacerbate the problems of SIL among residents in long-term care homes (LTCHs). However, some LTCHs have developed promising best practices (PBPs) to respond to the current situation and prepare for future pandemics. Key aspects of such practices revolve around maintaining and strengthening social connections between residents and their families which helps to reduce SIL. This scoping review looks at existing PBPs that have been implemented to reduce SIL among LTCH residents during the most recent pandemics. METHODS AND ANALYSIS: . In addition, we will also apply the Joanna Briggs Institute Reviewers' 'Methodology for Scoping Reviews'. Ten electronic databases and grey literature will be searched for articles published from January 2003 to March 2021 in either English or French. Two reviewers will independently screen titles and abstracts and then full texts for final inclusion. Data will be extracted using a standardised form from 'Evidence for Policy and Practice Information'. The results will be presented in a tabular form and will be summarised and interpreted using a narrative synthesis. ETHICS AND DISSEMINATION: Formal ethical approval is not required as no primary data are collected. Findings will be used to develop a solid knowledge corpus to address the challenges of SIL in LTCHs. Our findings will help to identify cutting edge practices, including technological interventions that could support health services in addressing SIL in the context of LTCHs and our ageing society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.138 | 0.127 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".