Effective strategies and interventions for engaging at-risk youth: a rapid systematic review of the literature
Bibliographic record
Abstract
Abstract Purpose Family violence, including physical abuse, sexual abuse, and exposure to intimate partner violence, has been linked to mental health problems and increased substance abuse in youth. This rapid systematic review evaluated the most recent literature on effective strategies and/or interventions to engage youth at-risk due to family violence, mental health problems, and/or substance abuse. Methods A rapid systematic review of the literature on engaging youth identified as at-risk for mental health difficulties, substance abuse, or family violence in interventions was undertaken. Searches were run in PsycINFO, CINAHL, SocINDEX, Family & Society Studies Worldwide and Social Work Abstracts. To be included, studies had to be quantitative or qualitative, examine youth-targeted strategies aimed at increasing engagement with interventions and have a target population aged between 12 to 18 years old. Studies published prior to 2014 were excluded. Results A total of 13 studies were retrieved that included 2,527 high-risk youth. Strategies were identified and categorized based on the following themes: (1) Technology-based, (2) Experiential therapy-based, (3) Counselling-based, (4) Program-based and (5) Other engagement strategies. Technology-based, experiential therapy-based and program-based strategies showed high levels of engagement in at-risk youth. Counselling-based strategies demonstrated variable outcomes between studies. Conclusions Many of the engagement strategies retrieved in this evidence assessment were reported to be successful, suggesting that there is no single best approach to engaging at-risk youth. A variety of strategies can be combined and tailored to fit the individual needs of youth and the resources available to the intervention program at that time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".