FC3: “Empowering Health & Social Service Providers in Addressing Social Isolation & Loneliness in Older Adults”
Bibliographic record
Abstract
“Social isolation among older adults is associated with increased change of premature death; depression; dementia, disability from chronic diseases; poor mental health; increased use of health and support services; reduced quality of life; poor general health; and an increased number of falls.” (National Academies of Sciences, Engineering, and Medicine (2020).Without question, the global pandemic has significantly exacerbated both the prevalence and awareness of social isolation and loneliness as a growing health and societal challenge for older populations.“Because of growing calls for Canada’s health-care systems to identify, prevent and mitigate loneliness as part of COVID-19-related public health efforts, there is a unique opportunity to build capacity to identify and intervene with older adults who are experiencing social isolation or loneliness.” National Institute on Aging (2022).Over the past two decades, the Canadian Coalition for Seniors’ Mental Health (CCSMH) has developed a number of internationally recognized clinical guidelines in support of mental health for older adults. CCSMH is responding to the growing mental health crisis of isolation and loneliness with the development of evidence-based guidelines, to support the vital work of health and social service providers across Canada. The focus of these guidelines is to develop a broad range of evidence-based, manageable, and stepped care approaches to identify and address social isolation and loneliness in older adults. It is recognized that this topic is extremely complex and vast in potential scope. Through the guidance of a national working group of experts, these guidelines will draw upon both academic and grey literature, as well as on the experience of a diversity of health and social service providers, older adults, and their caregivers. This project will also provide guidance, promoting wellness and reducing the risk of social isolation with targeted messaging, knowledge translation and useful tools for supporting social connection among those at highest risk.This presentation will share the Guidelines’ preliminary recommendations, as well as data from two national surveys alongside other insights gained from ongoing research and stakeholder 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.053 | 0.017 |
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".