Self-management needs of Irish adolescents with Juvenile Idiopathic Arthritis (JIA): how can a Canadian web-based programme meet these needs?
Bibliographic record
Abstract
BACKGROUND: Juvenile Idiopathic Arthritis (JIA) affects over 1000 children and adolescents in Ireland, potentially impacting health-related quality-of-life. Accessible self-management strategies, including Internet-based interventions, can support adolescents in Ireland where specialist rheumatology care is geographically-centralised within the capital city. This study interviewed adolescents with JIA, their parents, and healthcare professionals to (i) explore the self-management needs of Irish adolescents; and (ii) evaluate the acceptability of an adapted version of a Canadian JIA self-management programme (Teens Taking Charge: Managing Arthritis Online, or TTC) for Irish users. METHODS: = 14.19 years), and predominantly female (62.5%). Participants identified the needs of adolescents with JIA and evaluated the usefulness of the TTC programme. Data were analysed using a thematic analysis approach. RESULTS: Five themes emerged: independent self-management; acquiring skills and knowledge to manage JIA; unique challenges of JIA in Ireland; views on web-based interventions; and understanding through social support. Adolescents acknowledged the need for independent self-management and gradually took additional responsibilities to achieve this goal. However, they felt they lacked information to manage their condition independently. Parents and adolescents emphasised the need for social support and felt a peer-support scheme could provide additional benefit to adolescents if integrated within the TTC programme. All participants endorsed the TTC programme to gain knowledge about JIA and offered suggestions to make the programme relevant to Irish users. CONCLUSIONS: There is scope for providing easily-accessible, accurate information to Irish families with JIA. The acceptability of adapting an existing JIA self-management intervention for Irish users was confirmed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".