Social isolation and loneliness: the new geriatric giants: Approach for primary care.
Bibliographic record
Abstract
OBJECTIVE: To review the problems of social isolation, loneliness, and social vulnerability in older adults and the associated risks, and to help primary care providers identify patients at risk and recommend effective interventions. SOURCES OF INFORMATION: and associated key words for relevant English-language articles. References of identified articles were also hand searched. A separate search of the gray literature using Google was conducted to find policy documents and knowledge translation materials from relevant organizations. The search covered relevant articles from the 10 years before June 2019. MAIN MESSAGE: Social isolation, loneliness, and social vulnerability are very common in older adults and are associated with considerable morbidity and mortality, comparable to established risk factors such as smoking, alcohol consumption, obesity, and frailty. Numerous interventions addressing loneliness and social isolation have been studied: social facilitation (including technology), exercise, psychological therapies, health and social services, animal therapy, befriending, and leisure and skill development. However, current evidence of effectiveness is limited. A patient-centred approach is essential to the selection of interventions. The needs of underserviced and marginalized populations, including new immigrants, older adults identifying as LGBTQ+ (lesbian, gay, bisexual, transgender, queer or questioning, and related communities), Indigenous seniors, and seniors living in poverty, as well as the needs of long-term care residents and older caregivers, require further evaluation. CONCLUSION: Social isolation, loneliness, and social vulnerability are common problems in older adults and have important health consequences. Family physicians are uniquely positioned to identify lonely and socially isolated older adults and to initiate services.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".