Supporting Teens with Chronic Pain to Obtain High School Credits: Chronic Pain 35 in Alberta
Bibliographic record
Abstract
Chronic pain is a significant problem in children and teens, and adolescents with chronic pain often struggle to attend school on a regular basis. We present in this article a novel program we developed that integrates attendance at a group cognitive-behavioural chronic pain self-management program with earning high school credits. We collaborated with Alberta Education in the development of this course, Chronic Pain 35. Adolescents who choose to enroll are invited to demonstrate their scientific knowledge related to pain, understanding of and engagement with treatment homework, and demonstrate their creativity by completing a project, which demonstrates at least one concept. Integrating Chronic Pain 35 into an adolescent's academic achievements is a creative strategy that facilitates the engagement of adolescents in learning and adopting pain coping techniques. It also helps teens to advocate for themselves in the school environment and improve their parents' and teachers' understanding of adolescent chronic pain. This is one of the first successful collaborations between a pediatric health program and provincial education leaders, aimed at integrating learning and obtaining school credit for learning about and engaging in health self-management for teens. The authors hope this paper serves as an effective reference model for any future collaborating programs aimed at supporting teens with chronic pain to obtain high school credits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".