Impact of a peer-support programme to improve loneliness and social isolation due to COVID-19: does adding a secure, user friendly video-conference solution work better than telephone support alone? Protocol for a three-arm randomised clinical trial
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has forced the implementation of physical distancing and self-isolation strategies worldwide. However, these measures have significant potential to increase social isolation and loneliness. Among older people, loneliness has increased from 40% to 70% during COVID-19. Previous research indicates loneliness is strongly associated with increased mortality. Thus, strategies to mitigate the unintended consequences of social isolation and loneliness are urgently needed. Following the Obesity-Related Behavioural Intervention Trials model for complex behavioural interventions, we describe a protocol for a three-arm randomised clinical trial to reduce social isolation and loneliness. METHODS AND ANALYSIS: A multicentre, outcome assessor blinded, three-arm randomised controlled trial comparing 12 weeks of: (1) the HOspitals WoRking in Unity ('HOW R U?') weekly volunteer-peer support telephone intervention; (2) 'HOW R U?' deliver using a video-conferencing solution and (3) a standard care group. The study will follow Consolidated Standard of Reporting Trials guidelines.We will recruit 24-26 volunteers who will receive a previously tested half day lay-training session that emphasises a strength-based approach and safety procedures. We will recruit 141 participants ≥70 years of age discharged from two participating emergency departments or referred from hospital family medicine, geriatric or geriatric psychiatry clinics. Eligible participants will have probable baseline loneliness (score ≥2 on the de Jong six-item loneliness scale). We will measure change in loneliness, social isolation (Lubben social network scale), mood (Geriatric Depression Score) and quality of life (EQ-5D-5L) at 12-14 weeks postintervention initiation and again at 24-26 weeks. ETHICS AND DISSEMINATION: Approval has been granted by the participating research ethics boards. Participants randomised to standard care will be offered their choice of telephone or video-conferencing interventions after 12 weeks. Results will be disseminated through journal publications, conference presentations, social media and through the International Federation of Emergency Medicine. TRIAL REGISTRATION NUMBER: NCT05228782.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.065 | 0.011 |
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".