Community‐based responses to loneliness in older people: A systematic review of qualitative studies
Bibliographic record
Abstract
In many countries across the world, older people are one of the groups most vulnerable to loneliness. Community-based responses are well placed to support and enhance pre-existing coping strategies in older people. However, the evidence base of these responses remain scattered and obscured, particularly in relation to their design and reasons behind their success. In this systematic review, we focus on qualitative studies on community-based responses to loneliness among older people to learn how these responses work in practice with in-depth details. At the end of a systematic searching and screening process, 17 studies conducted in five countries (Australia, Canada, New Zealand, Spain and the UK) published in English were selected and reviewed initially in October 2020 and then updated at the end of August 2021. Three themes were identified as being most valuable to addressing loneliness in a specific community, namely, autonomy, new social connections, and belonging. These interventions were also employed according to three primary considerations: what the community lacked, how that community experienced loneliness, or a combination of both. Several implications for policymakers and future research emerged, urging future interventions to take a more contextual approach that encompasses community-level considerations before establishing a user-led and tailored setting that facilitates social engagement.
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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 | 0.043 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".