Social Cohesion, Mental Well-Being, and the Role of Smart Technology and Pet Ownership Among Social Housing Residents: Cross-Sectional Cohort Study
Bibliographic record
Abstract
Background: Smart technology has been shown to have varied effects on social cohesion and mental well-being. There has been very little research on associations between pet ownership and social cohesion and mental well-being. Objective: This study aimed to explore associations between social cohesion and mental well-being and ownership of different forms of smart technology, dogs, and cats in a sample of adult social housing occupants in Cornwall, United Kingdom. Methods: This was a cross-sectional study that collected data on people's living environment, health, and well-being, including the Short Warwick-Edinburgh Mental Wellbeing Scale and an 8-item social cohesion scale. Participants were social housing residents in Cornwall in the South West of the United Kingdom. We used cross-sectional regression analyses to explore associations between people's ownership of different forms of smart technology and pets, and their reported levels of social cohesion and mental well-being. Results: There were no statistically significant associations between social cohesion and ownership of either smart technology or pets. Unadjusted regressions for mental well-being showed an association with owning a smartphone. However, after adjusting for age, gender, and socioeconomic status, this effect was no longer significant. Those who owned any smart technology (b=1.76, 95% CI 0.06-3.45; P=.04) and those who owned a games console (b=2.39, 95% CI 0.59-4.19, P=.01) had significantly higher levels of mental well-being, after adjusting for age, gender, and socioeconomic status; the effect held after social cohesion was added to the model. Counterintuitively, owning two or more dogs was associated with lower levels of mental well-being (b=-2.12, 95% CI -4.06 to -0.19; P=.03) compared with owning no dogs, after adjusting for age, gender, socioeconomic status, and social cohesion. However, there were no significant differences in mental well-being related to cat ownership. Conclusions: Previous research suggests that the beneficial effects of smart technology are context-dependent, and our results support that. While we did not observe significant effects on social cohesion, owning any smart technology or a games console specifically was associated with well-being benefits. There are limited data on pet ownership and social cohesion or mental well-being: our data suggest there is no strong relationship with social cohesion but owning multiple dogs can have negative effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".