Combatting Older Adult Loneliness: It Takes a (Blended) Village
Bibliographic record
Abstract
Older adults have been thrown into the spotlight of the COVID-19 pandemic and the bright lights have exposed both societies’ admirable and deplorable traits. We have seen stories of heart-warming compassion and deep-rooted ageism. From the appalling #boomerremover hashtag to the calls for mandatory quarantines for those over 70 years of age, public responses to COVID-19 demonstrate the role of age and (dis)ability in amplifying social and spatial inequalities. Although these reactions are unfounded, unethical, and have not received widespread political support, they do highlight the distressing interrelation of several truths: society at large is aging; older adults are at higher risk for developing more serious complications from COVID-19; and the social and physical infrastructure of cities has not been built to support the needs of older adults. In addition to the risks of COVID-19, the confluence of these three realities has potentially exacerbated a second public health crisis: loneliness. And as in the case of COVID-19, older adults are particularly susceptible. In this chapter we examine the relationship between COVID-19, social distance, social isolation, and loneliness with a focus on the older adult experience in urban and suburban environments. In addition to outlining the risks faced by older adults in times of crisis, we explore opportunities to strengthen social bonds while physically distancing through the development of blended communities or virtual retirement villages. Using the experience of the Oakridge Seniors Association in suburban Calgary, we offer targeted recommendations for community leaders and policy makers on how to minimize risk and maximize social cohesion by embracing communication technology while remembering the importance of human interaction. (Chapters Eleven and Twelve also explore the theme of self-organization in the face of the pandemic, but from the perspective of different national contexts and social categories.)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".