Integrating the Voices of Youth with Lived Experience as Co-researchers to Improve Research and Practice Approaches to Childhood Experiences of Intimate Partner Violence
Bibliographic record
Abstract
Abstract Purpose Childhood experiences of intimate partner violence (CEIPV) is common, but youth voices are underrepresented in such research, and little is known about what they find helpful in counselling. The purpose of this research was to engage youth with lived experience to address the following questions: (1) What do youth with CEIPV identify as key competencies needed for CEIPV service providers? And (2) How can youth participatory action research (YPAR) (and participatory methods) be utilized to enhance research on CEIPV? This article is focused on the YPAR process and addressing the second research question. Methods Utilizing a YPAR approach, 12 Canadian youth were engaged as members of the research team to include their voices, perspectives, and experiences in addressing the research questions. Results Youth researchers highlighted strategies and principles to engage youth meaningfully in YPAR. These were: (1) recognizing and valuing lived experience as expertise; (2) recognizing the diversity in youths’ experiences, skills, and strengths; (3) creating a safe space with no judgement; and (4) having adult researchers committed to the process and not just the outcomes of the research. Conclusions The YPAR process used in the current study is an example of how youth with lived expertise successfully engage in research. Reflecting on what worked in the current study, youth researchers provided recommendations to improve youth engagement. When safety and relationships are prioritized, and youth feel heard and respected, they note finding value in participatory research to counter the impact CEIPV has had on their lives.
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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 | 0.075 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".