A longitudinal, mixed methods study exploring the impact of civic engagement on psychosocial outcomes across early to mid adulthood.
Bibliographic record
Abstract
Civic engagement during emerging adulthood plays a pivotal role in fostering a sense of community responsibility, providing a sense of societal purpose, and contributes to improved psychological adjustment. In this mixed-method longitudinal study, we further explored how civic engagement and psychological adjustment codevelop across emerging adulthood. Participants were drawn from The Future's Study, a Canadian longitudinal study capturing the transition to adulthood in Southwestern Ontario. The sample was predominantly White (81%), female identifying (71%), and largely affluent with 5.8% reporting lower than average family income. At ages 23, 26, and 32, participants completed measures of civic engagement, depression, and optimism; at age 26, participants had the opportunity to also complete a life story interview where they were asked to recount a key community scene from their lives and reflect on its impact. Random intercept cross-lagged panel models illustrated that civic engagement across ages 23-32 reduced loneliness concurrently and longitudinally. No cross-lagged associations were found for depression or optimism. Four themes illuminated the role of civic engagement in buffering against loneliness during emerging adulthood and into midlife: community unites people through a shared vision, fosters meaningful and long-lasting connections, solidifies the importance of leaving a legacy for future generations, and contributes to personal growth via insight into others' lives, which illuminated an awareness of one's own social advantages and privilege. These results illustrate that the pathway between increased civic engagement and reduced loneliness may be due, in part, to intrinsic and collective motives that tie together personal growth, identity, and generativity. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".