Asking the experts: Exploring the self‐management needs of adolescents with arthritis
Bibliographic record
Abstract
OBJECTIVE: To explore the self-management needs of adolescents with juvenile idiopathic arthritis and the acceptability of a Web-based program of self-management aimed at improving quality of life. METHODS: A descriptive qualitative design was used. A convenience sample of 36 adolescents (male and female) who varied in age, disease onset subtype, and disease severity were recruited from 4 Canadian tertiary care pediatric centers. Individual (n=25) and 3 focus-group (n=11) interviews were conducted with adolescents using semistructured interview guides. After each interview session, the audiotaped interview data were transcribed verbatim. NUD*IST 6.0 was used to assist with the sorting, organizing, and coding of the data. Data were organized into categories that reflected emerging themes. RESULTS: Adolescents articulated how they developed effective self-management strategies through the process of "letting go" from others who had managed their illness (health care professionals, parents) and "gaining control" over managing their illness on their own. The 2 strategies that assisted in this process were gaining knowledge and skills to manage the disease and experiencing understanding through social support. Five further subthemes emerged around skills to manage the disease, including knowledge and awareness about the disease, listening to and challenging care providers, communicating with the doctor, managing pain, and managing emotions. CONCLUSION: Adolescents were united in their call for more information, self-management strategies, and meaningful social support to better manage their arthritis. They believed that Web-based interventions were a promising avenue to improve accessibility and availability of these interventions.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".