Youth-identified considerations, drivers, and strategies for meaningful youth engagement in child maltreatment research
Bibliographic record
Abstract
Academic research is rapidly shifting to incorporate and emphasise the expertise of youth with lived experience. However, not all research areas have been equally successful in engaging youth in research processes. Youth engagement in child maltreatment (CM) research has been sparse. To address this gap, the Better Together Child Maltreatment Priority Setting Partnership is engaging youth with lived experience, along with caregivers, clinicians, and researchers, to determine the top 10 priorities for CM research in Canada. A Youth Consultant Panel (YCP) was assembled to inform all aspects of the project from the perspectives of youth. In this discussion article, the YCP and researchers share considerations, drivers, and strategies for meaningful youth engagement in CM research based on experiences of research engagement in varying roles. First, considerations include power imbalances, potential distress and retraumatisation, and unsafe disclosure. Second, drivers for engagement in research are described – the ability to break cycles of violence, to leverage lived experience toward meaningful change, and to build new, safe relationships with peers and researchers. Finally, specific strategies to facilitate meaningful youth engagement are offered: (1) checking researcher readiness; (2) checking youth readiness; (3) considering the approach to engagement and facilitation; and (4) providing appropriate compensation and credit. This article demonstrates how engagement in CM research is intrinsically an act of resistance against past, present, and future violence. • Youth engagement in child maltreatment research has been sparse. • Power imbalances and other considerations may deter engagement. • Engagement is facilitated by the ability to break cycles of violence and other drivers. • Purposeful strategies can enable engagement and its transformative potential for research and youth.
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 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 | 0.099 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".