Neighborhood Disorder and Health-Related Work Absences: Perceived Control and Neighborhood Trust as Explanatory Mechanisms
Bibliographic record
Abstract
The current research argues that people residing in disordered neighborhoods will tend not to trust their neighbors and perceive less control over life, which will in turn increase the risk of health-related work absences. Researchers also suggest that lower trust in neighbors and perceived control will strengthen the association between living in disordered neighborhoods and risk of health-related work absences. To address these questions, we examine a national study of Canadian workers gathered at the individual level in September of 2019 (N=2,524). Multinomial regression models show that perceptions of neighborhoods as disordered are associated with a greater likelihood of frequent health-related work absences. Reduced trust in neighbors and perceived control largely explain this association, but these factor do not moderate the association. This research contributes to the study of neighborhoods and health by showing that adverse health effects of disordered neighborhoods can have subsequent socioeconomic implications through increased health-related work absences.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".