Mental healthcare for children with chronic conditions: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: To explore parent perspectives on accessing mental healthcare for children with a chronic physical health condition. DESIGN: Qualitative research using semistructured interviews and Framework Analysis. Rankings were used to select attributes for a Discrete Choice Experiment (DCE). SETTING: Four specialty outpatient clinics (diabetes, epilepsy, bronchiectasis unrelated to cystic fibrosis and epidermolysis bullosa) at an Australian tertiary paediatric hospital. PARTICIPANTS: Eighteen parents of children with a chronical physical health condition. RESULTS: Most parents identified the child's general practitioner and/or hospital team as an initial pathway to seek help if they were worried about their child's mental health. Parents see mental healthcare as part of care for the whole child and want the outpatient clinics to proactively discuss child and family mental health, as well as refer to appropriate services as needed. The hospital being a familiar, child-friendly environment was identified as a key reason the hospital might be a desired place to access mental healthcare, as previous research has found. Six attributes of mental health services were identified as important and will be included in an upcoming DCE: travel time, cost, wait time, available hours, knowledge of physical health condition, and recommendation. CONCLUSIONS: This study highlights the opportunity presented in specialist outpatient clinics to address the often unmet mental healthcare needs of children with chronic physical health conditions. Parents identified practical ways for outpatient clinics to better facilitate access to mental healthcare. These will be further explored through a quantitative study of parent preferences.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".