Social inequity in ageing in place among older adults in Organisation for Economic Cooperation and Development countries: a mixed studies systematic review
Bibliographic record
Abstract
BACKGROUND: Most older adults wish to remain in their homes and communities as they age. Despite this widespread preference, disparities in health outcomes and access to healthcare and social support may create inequities in the ability to age in place. Our objectives were to synthesise evidence of social inequity in ageing in place among older adults using an intersectional lens and to evaluate the methods used to define and measure inequities. METHODS: We conducted a mixed studies systematic review. We searched MEDLINE, EMBASE, PsycINFO, CINAHL and AgeLine for quantitative or qualitative literature that examined social inequities in ageing in place among adults aged 65 and older in Organisation for Economic Co-operation and Development (OECD) member countries. Results of included studies were synthesised using qualitative content analysis guided by the PROGRESS-Plus framework. RESULTS: Of 4874 identified records, 55 studies were included. Rural residents, racial/ethnic minorities, immigrants and those with higher socioeconomic position and greater social resources are more likely to age in place. Women and those with higher educational attainment appear less likely to age in place. The influence of socioeconomic position, education and social resources differs by gender and race/ethnicity, indicating intersectional effects across social dimensions. CONCLUSIONS: Social dimensions influence the ability to age in place in OECD settings, likely due to health inequalities across the lifespan, disparities in access to healthcare and support services, and different preferences regarding ageing in place. Our results can inform the development of policies and programmes to equitably support ageing in place in diverse populations.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".