There Is No Community Here: Living Alone, Place, and Older Peoples' Risk of Social Isolation
Bibliographic record
Abstract
Abstract Existing research has identified significant risk factors for experiencing social isolation in later life including chronic health conditions, mobility impairments, and living alone among others. Although many older people who live alone maintain active social lives, living alone remains a top predictor of social isolation. Less is known about other types of risk factors, such as place-based risks and social exclusion. Despite calls to examine the role of place and social exclusion in social isolation risk, few studies have investigated the links. Models of isolation risk have often omitted place-based factors and social exclusion and focused largely on individual-level risks. In order to address these gaps, this paper presents the findings of 17 in-depth, qualitative interviews with community-dwelling older people who live alone (aged 65-93). Participants were recruited using a theoretical sampling strategy to ensure that a diverse range of neighbourhood types were represented among the participants (e.g., walkable vs. car-dependent neighbourhoods). Interview transcripts were analyzed using a constructivist grounded approach resulting in several major themes. Participants described aspects of their local environments as shaping their risk of isolation including infrastructure and amenities delivered in place, and neighbourhood makeup, among others. These themes are further examined through the lens of place-based exclusion and used to conceptualize how dimensions of both place and social exclusion fit into the model of known isolation risk factors. An adapted model of risk is presented to guide future research and intervention planning.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".