Collaborating with a Youth Council to Improve Chronic Pain Resources
Bibliographic record
Abstract
Background: There is a recognized need to involve people with lived experience of chronic pain when developing chronic pain resources. Aims: The aim of this study was to develop, implement, and evaluate a short-term youth council focused on eliciting youths' recommendations for key features of chronic pain informational resources. Methods: In this mixed methods instrumental case study, demographic data were collected via Survey Monkey®. Select Patient-Reported Outcomes Measurement Information System® brief measures were used to provide context regarding pain impact within this group. Participants completed an initial interview, which informed youth council workshop delivery. Over two youth council workshops, participants reviewed select informational resources and identified key features of chronic pain resources. Participants evaluated their involvement experience during a second interview. Qualitative data were transcribed and analyzed using directed content analysis. Member-checking occurred during a third workshop, held virtually. Results: Seven youth self-identifying as girl/woman or demi-girl participated. The youth were satisfied with the youth council experience, highlighting the importance of meeting others, a relaxed environment, and participating in valuable work. A list of youth-identified key features for informational resources was created through the workshops, which includes considerations for audience groups, content, and presentation. Conclusion: Participants' input into youth council development and meeting others with lived experience contributed to a safe and supportive involvement experience. Youth council involvement supported the development of preliminary recommendations for chronic pain informational resources.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".