Time to reshape our delivery of primary care to vulnerable older adults in social housing?
Bibliographic record
Abstract
Housing is a social determinant of health that fundamentally affects health and wellbeing.1 A UK study found housing to be a unique predictor of health outcomes, after accounting for income. 1 Although social housing can improve health through the financial benefit of lower rent, 2 it also has negative impacts due to building disrepair, 2 and policies resulting in clusters of residents with poor mental health and substance issues.3 Social housing exists in many formats across the globe: council estates in the UK; projects or public housing in the US; social housing in Canada; social or public housing in Australia; and state housing in New Zealand. MANY OLDER ADULTS LIVE IN SOCIAL HOUSING AND ARE VULNERABLEIn the US, it is predicted that, by 2035, one in three public housing units will be occupied by an individual aged ≥65 years, 4 while in the UK that is already the situation.5 In Ontario, Canada alone, approximately 75 000 older adults live in social housing.6 Older adults on low incomes are a vulnerable population with increased risk of chronic disease, falls, social isolation, 7 and poor outcomes from COVID-19.In-person restrictions have heightened the importance of internet-based solutions for access to health care, information, basic necessities (for example, grocery apps), and friends and family.However, older adults on low incomes have limited ability to access services launched specifically for COVID-19 due to poor health literacy 8 and lack of internet.9 This compounded social isolation can have a detrimental impact on older adults' mental health and resilience, especially in social housing.
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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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.028 | 0.035 |
| Insufficient payload (model declined to judge) | 0.015 | 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".