Understanding the Role of Prosocial Behavior in Youth Mental Health: Findings from a Scoping Review
Bibliographic record
Abstract
Background: Mental health during a person’s adolescence plays a key role in setting the stage for their mental health over the rest of their life. Hence, initiatives that promote adolescents’ wellbeing are an important public health goal. Helping others can take a variety of forms, and the literature suggests that helping others can positively impact a person’s wellbeing. However, there is a lack of data that synthesizes the impact of helping others on adolescents’ wellbeing. Therefore, this review aims to synthesize the available evidence related to helping others and to youth wellbeing. Methods: A scoping review search was undertaken with no date restrictions. CINAHL, Medline and PyschINFO, were searched for studies that analyzed the relationship between helping others and youth mental health. Results: Data from 213 papers were included in the scoping review. Three main themes were observed: (1) the relationship between helping others and mental health outcomes among youths (positive and negative); (2) factors associated with youth engagement in prosocial behavior (facilitators and barriers); (3) the impact of interventions related to helping others, and to youth mental health (positive and negative). Conclusions: An overwhelmingly positive relationship exists between youth prosocial behavior and its influence on youth mental health.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".