On the Association of Community Belonging With Over Time Changes in Self‐Rated Health and Mental Health Since the COVID‐19 Pandemic
Bibliographic record
Abstract
This study evaluates how community belonging influences over time changes in self-rated health (SRH) and self-rated mental health (SRMH) after the COVID-19 pandemic using a large Canadian population survey (N = 9013, response rate 25%). The study uses descriptive analysis and a set of ordered logistic regressions. The results suggest that 1 year after the height of the pandemic, most of the respondents consider their SRH and SRMH as excellent, good, or fair, with 89.1% for SRH and 85.7% for SRMH. By contrast, only 10.8% and 14.3% of respondents reported poor and very poor SRH and SRMH. Furthermore, about 25%-29% of respondents reported that their SRH and SRMH became better over time, and only 11%-12% reported worsening SRH and SRMH, while the rest reported no changes. Regression results suggest that higher levels of community belonging are associated with higher SRH and SRHM. For instance, reporting strong community belonging is associated with higher SRH (OR = 4.734, 95% CI [4.004, 5.598]) and SRMH (OR = 5.778, 95% CI [4.879, 6.842]). Moreover, they suggest that a higher level of community belonging is associated with perceived improvement over time in SRH and SRMH. For example, reporting strong community belonging is associated with more improvement over time in SRH (OR = 2.105, 95% CI [1.757, 2.523]) and SRHM (OR = 1.927, 95% CI [1.607, 2.312]). The study concluded by discussing theoretical, policy, and methodological implications of these findings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".