Enhancing researcher capacity to engage youth in research: Researchers’ engagement experiences, barriers and capacity development priorities
Bibliographic record
Abstract
BACKGROUND: There is increasing emphasis on engaging youth in research about youth, their needs, experiences and preferences, notably in health services research. By engaging youth as full partners, research becomes more feasible and relevant, and the validity and richness of findings are enhanced. Consequently, researchers need guidance in engaging youth effectively. This study examines the experiences, needs and knowledge gaps of researchers. METHODS: Eighty-four researchers interested in youth engagement training were recruited via snowball sampling. They completed a survey regarding their youth engagement experiences, attitudes, perceived barriers and capacity development needs. Data were analysed descriptively, and comparisons were made based on current engagement experience. RESULTS: Participants across career stages and disciplines expressed an interest in increased capacity development for youth engagement. They had positive attitudes about the importance and value of youth engagement, but found it to be complex. Participants reported requiring practical guidance to develop their youth engagement practices and interest in a network of youth-engaged researchers and on-going training. Those currently engaging youth were more likely to report the need for greater appreciation of youth engagement by funders and institutions. CONCLUSIONS: Engaging youth in research has substantial benefits. However, skills in collaborating with youth to design, conduct and implement research have to be learned. Researchers need concrete training and networking opportunities to develop and maximize these skills. They also need mechanisms that formally acknowledge the value of engagement. Researchers and those promoting youth engagement in research are encouraged to consider these findings in their promotion and training endeavours.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.181 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".