<i>iCanCope with Pain</i>™: User‐Centred Design of a Web‐ and Mobile‐Based Self‐Management Program for Youth with Chronic Pain Based on Identified Health Care Needs
Bibliographic record
Abstract
BACKGROUND: While there are emerging web-based self-management programs for children and adolescents with chronic pain, there is currently not an integrated web- and smartphone-based app that specifically addresses the needs of adolescents with chronic pain. OBJECTIVES: To conduct a needs assessment to inform the development of an online chronic pain self-management program for adolescents, called iCanCope with Pain™. METHODS: A purposive sample of adolescents (n=23; 14 to 18 years of age) was recruited from two pediatric chronic pain clinics in Ontario. Interdisciplinary health care providers were also recruited from these sites. Three focus groups were conducted with adolescents (n=16) and one with pediatric health care providers (n=7). Individual adolescent interviews were also conducted (n=7). RESULTS: Qualitative analysis uncovered four major themes: pain impact; barriers to care; pain management strategies; and transition to adult care. Pain impacted social, emotional, physical and role functioning, as well as future goals. Barriers to care were revealed at the health care system, patient and societal levels. Pain management strategies included support systems, and pharmacological, physical and psychological approaches. Transition subthemes were: disconnect between pediatric and adult systems; skills development; parental role; and fear⁄anxiety. Based on these identified needs, the iCanCope with Pain™ architecture will include the core theory-based functionalities of: symptom self-monitoring; personalized goal setting; pain coping skills training; peer-based social support; and chronic pain education. CONCLUSIONS: The proposed iCanCope with Pain™ program aims to address the self-management needs of adolescents with chronic pain by improving access to disease information, strategies to manage symptoms and social support.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".