Social and mental health pathways to institutional trust: A cohort study
Bibliographic record
Abstract
OBJECTIVE: Trust in institutions such as the government is lower in the context of mental health problems and socio-economic disadvantage. However, the roles of structural inequality, interpersonal factors, and mental health on institutional trust remain unclear. This study aimed to examine the associations of social and mental health factors, from early life to adulthood, with institutional trust. METHOD: Participants (n = 1347; 57.2 % female) were from the population-based Québec Longitudinal Study of Child Development (1997-2021). Trust in 13 institutions was self-reported at age 23. Predictors were 20 social and mental health factors during early life, adolescence, and adulthood. Associations were examined with linear regressions corrected for false discovery rate. Pathways were explored using the temporal Peter-Clark algorithm. RESULTS: Early-life factors associated with lower levels of trust were male sex, racialized minority status, low household income, and maternal history of depression and antisocial behaviors. After adjusting for early-life factors, adolescence factors associated with lower levels of trust were internalizing and externalizing problems, bullying exposure, and school difficulties. Independently of early-life or adolescence factors, adulthood factors associated with lower levels of trust were perceived stress, psychotic experiences, suicidal ideas, and seeking professional help, whereas greater social connectedness was associated with greater trust. Temporal Peter-Clark analyses identified social connectedness and psychotic experiences as potential proximal determinants of institutional trust. CONCLUSION: This study identified factors related to structural inequality, interpersonal relationships, and mental health over development that were associated with institutional trust. Interventions aimed at promoting social connectedness and equity may improve institutional trust and wellbeing.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".