Loneliness and sense of community are not two sides of the same coin: Identifying different determinants using the 2019 Nova Scotia Quality of Life data
Bibliographic record
Abstract
The purpose of this study was to explore the relative importance of lifestyle factors and living conditions when predicting loneliness and sense of community (SOC) in a representative sample of 12,871 participants from Nova Scotia collected in 2019. Using multiple regression and measures of relative importance based on the Lindeman, Merenda and Gold (lmg) method, we identified which variables are most important to predicting measures of loneliness and SOC. Twenty-two predictors accounted for 46% of the variance in SOC and the top 10 predictors accounted for 36% of the variance: satisfaction with quality of the natural environment in the neighborhood (ri = 0.09), life satisfaction (ri = 0.05), number of neighbors one can rely on (ri = 0.05), confidence in institutions (ri = 0.05), feeling better off due to government policy or programming (ri = 0.04), feeling safe walking in neighborhood after dark (ri = 0.03), mental health (ri = 0.02), number of friends one can rely on (ri = 0.02), volunteering (ri = 0.02), and perceptions of time adequacy (ri = 0.02). Only six of these variables were also the top predictors of loneliness. These results show that both community- and individual-level variables are substantial predictors of social well-being. The effect sizes differ between models, which suggests that there may be important predictors of loneliness that we have not accounted for. This study may inform community-level programming and policy that seeks to promote social well-being for individuals and their communities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".