THE “EPIDEMIC” OF OLDER ADULT LONELINESS: PROBLEMS OF DIAGNOSTIC INTERVENTION AND CRITICAL SOCIOLOGICAL RESEARCH
Bibliographic record
Abstract
Abstract Loneliness, more than a feeling, has become a new ‘geriatric giant’ (Freedman & Nicolle 2020) and epidemic health crisis for older adults, affecting their physical, cognitive and emotional well-being. During the COVID epidemic, the intersecting effects of loneliness and isolation (often blurred in the literature) have intensified, as varying public health measures restricted visiting, gathering, routines and activities. While technical interventions, such as digital communication technologies (DCTs), tele-health meetings, online games, robotic pet companions and simulated presence therapy (SPT) are offered as beneficial aids, even where available or co-designed they tend to individualize and universalize loneliness. Professional, recreational and prescriptive interventions can also disregard the structural relations and socio-material environments that configure everyday lonely-making experiences over time. For both residential and community dwelling older adults, such experiences include lack of affordable housing, care-giver burden and insufficient community resources and planning. This presentation, drawing upon data and examples from senior health policy, loneliness surveys, national reports and qualitative research, reflects on these troubling matters in their complexity and heterogeneity. Conclusions explore the making of an ageist emotional economy that depicts and neglects older adults as inevitably lonely, while advocating for their rights to age in safe, healthy and socially connective ways.
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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 | 0.149 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.006 |
| Science and technology studies | 0.013 | 0.065 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".