What informs the choices young people living with chronic musculoskeletal pain make about their care? A qualitative analysis of focus groups with young people in Australia
Bibliographic record
Abstract
Purpose To explore how lived and care experiences of young people (aged 16–24 years) who experience chronic musculoskeletal pain (CMP) influence their choices about CMP care, and how and where a digital health solution (DHS) could support their care.Methods A cross-sectional, exploratory qualitative study involving 20 young people (16–24 years) experiencing CMP. Eight focus groups were conducted, guided by a focus group schedule. Data were analyzed using thematic analysis.Results Three main themes emerged describing young people’s experiences and CMP care choices. For each main theme we identified how a DHS could support their care: (1) “Experiences of living with and managing their CMP.” A DHS could buffer self-care needs by providing timely support and creating a sense of community. (2) “Experiences with healthcare providers and healthcare services.” An app-based DHS could potentially help to coordinate CMP care and support health services navigation. (3) “Young people’s choices about their CMP care options.” DHSs can support young people prioritize their CMP care options.Conclusions Understanding young people’s values, alongside their care needs is critical to delivering person-centred care. A tailored DHS can value-add to young people’s CMP care by helping to minimize the burden of self-care, health service navigation and interactions with healthcare providers.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".