Content Analysis of Perceived Social Isolation Remedies Shared in a National E-Survey (Preprint)
Bibliographic record
Abstract
BACKGROUND Older people are experts on what a quality social life looks like while living most at risk for COVID-19-related health harms. Older Canadians have helped raise awareness of the physical and mental detriments of social isolation. Remedial initiatives that build on older people’s lived experiences are also important initiatives. OBJECTIVE When public health restrictions were lifted in the Summer of 2022, we aimed to collect evidence grounded in older people’s everyday lived experiences about transitioning into open spaces while COVID-19 still lingered. METHODS This study was part of a larger e-survey project about mentally healthy living among 1,327 community-dwelling persons 60+ years of age. A sample stratified by age, sex, and education to approximate the Canadian population was asked: With COVID-19 public health measures lifting, based on your own experience, what would you suggest other older Canadians do to reduce social isolation? They responded as they saw fit. RESULTS Content analysis of 1,189 open-text messages revealed four calls to action: 1) Cultivating community; 2) Making room for what’s good; 3) Don’t let your guard down; and 4) Voiced out challenges. All four remedies were similarly endorsed, regardless of messengers’ age, sex, gender identity, and perceived health. Making room for what’s good seemed more amiable for those navigating newly open spaces without a chronic illness. Education level was linked with endorsing guarded social transitions. CONCLUSIONS While COVID-19 is no longer a global health risk, a worrisome proportion of older people still live more isolated lives. We encourage health and social care practitioners and older people themselves to share the messages identified in this study with more isolated others.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".