Novel Methods to Collect Meaningful Data From Adolescents for the Development of Health Interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health interventions are increasingly focused on young adolescents, and as a result, discussions with this population have become a popular method in qualitative research. Traditional methods used to engage adults in discussions do not translate well to this population, who may have difficulty conceptualizing abstract thoughts and opinions and communicating them to others. As part of a larger project to develop and evaluate a video game for risk reduction and HIV prevention in young adolescents, we were seeking information and ideas from the priority audience that would help us create authentic story lines and character development in the video game. To accomplish this authenticity, we conducted in-depth interviews and focus groups with young adolescents aged 10 to 15 years and employed three novel methods: Storytelling Using Graphic Illustration, My Life, and Photo Feedback Project. These methods helped provide a thorough understanding of the adolescents' experiences and perspectives regarding their environment and future aspirations, which we translated into active components of the video game intervention. This article describes the processes we used and the valuable data we generated using these three engaging methods. These three activities are effective tools for eliciting meaningful data from young adolescents for the development of health interventions.
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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: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.101 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it