Roadmap to the ‘Chronic Pain GPS for Adolescents’ Intervention
Bibliographic record
Abstract
OBJECTIVES: A biopsychosocial approach to understanding and treating pain is crucial; however, there are limited socially targeted interventions for adolescents with chronic pain (ACP). Peer support interventions implemented with other populations are associated with positive outcomes. ACPs perceive peer support to have high potential value. This study explored the preferences of ACP regarding the content and design of a group peer support intervention. METHODS: Fourteen ACP (M age : 15.21 y; 9 females; 3 males, 1 nonbinary, and 1 gender questioning) completed a virtual interview and survey. Interviews were analyzed using inductive qualitative content analysis, and surveys were analyzed using descriptive statistics. RESULTS: Adolescents described how they want to both talk and do activities together within a fun and casual environment with a facilitator present-ideally, someone with lived experience of chronic pain. Preferences were for a medium-sized group intervention that was in-person, at a consistent time on a weekday after school, and semi-structured. Barriers to attending and engaging in the potential group peer intervention were also discussed. DISCUSSION: ACPs desire a facilitated socially focused intervention that provides them with the opportunity to spend time with other ACPs. A group peer support environment where ACPs can provide and receive peer support through sharing their experiences with others who understand them as well as engage in activities was described. The findings from this study provide insights for the development of a group peer support intervention.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.005 |
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".