iCanCope with PainTM Norway : Cultural translation and feasibility testing of the Norwegian iCanCope with PainTM app aimed at reducing pain and improving health-related quality of life in a school-based population of adolescents with persistent pain
Bibliographic record
Abstract
Persistent pain has a high prevalence among adolescents. Pain has been shown to reduce all aspects of the adolescent’s health-related quality of life (HRQOL). Available pain-coping applications (apps) are rarely scientifically evaluated nor have health personnel in their development. Thus, there is a need to provide coping strategies in evidence- and theory-based app interventions aimed at reducing pain and increasing HRQOL among adolescents with persistent pain.\nThe iCanCope with PainTM app is originally from Canada and based on theory, identified healthcare needs and current best practices for pain self-management. There was a need for ensuring the app was appropriate for a school-based population of Norwegian adolescents with persistent pain. Hence, Paper I described the translation and cultural adaptation of the app into the Norwegian context and evaluated the app’s usability. The findings from Paper I secured a fundamental platform for further feasibility testing on a larger scale. Given the limited research evidence regarding the underlying mechanisms between pain and HRQOL in adolescents with persistent pain, Paper II described the experience of pain, HRQOL and self-efficacy among this study sample; and explored the association between pain intensity and HRQOL testing for self-efficacy as a possible mediator. Finally, in Paper III we determined the feasibility and explored possible differences in outcomes between the intervention and control groups of an 8-week intervention using the Norwegian iCanCope with PainTM app. Two papers have been published in peer-reviewed journals and one paper submitted, which together have established a coherence in research toward the overall objective of this thesis.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.006 | 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".