Process and experience of youth researchers within a Health Promoting Schools study in Nova Scotia, Canada
Bibliographic record
Abstract
Youth Participatory Action Research (YPAR) is an approach to research that engages youth across the research process. The peer researcher method is a technique used in YPAR where youth are trained in research and ethics to interview their peers. The purpose of this study was to: (i) describe the process of engaging youth as peer researchers in a Health Promoting Schools (HPS) and student engagement project and (ii) understand the peer researchers' perspectives of their experience throughout the project. Youth from across Nova Scotia, Canada in grades 7-10 (ages 12-16) were recruited as peer researchers in the Summer, 2022. The project included three stages: (i) peer researcher training, (ii) practicing, recruiting and conducting interviews and (iii) data interpretation workshop. To understand the peer researcher's experience, quantitative data were collected from an evaluation questionnaire. Outputs were produced using descriptive statistics. Qualitative data were collected through a focus group and interviews and analyzed using inductive content analysis. A total of 11 youth were recruited and completed peer researcher training. Most youth provided positive feedback on the training with a satisfaction score of 8.7/10. Qualitative analysis indicated benefits to the peer researchers including opportunities to build interview and social skills and learn about other's perspectives. This study provides a detailed overview of how to use a peer researcher method in a YPAR project to involve youth in research related to HPS and student engagement. The research also highlights the benefits of engaging youth in YPAR. Future research will report on the findings from the peer interviews.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".