Identifying the information and mental health service needs of children with cystic fibrosis
Bibliographic record
Abstract
Objective: Children with cystic fibrosis (CF) may experience elevated symptoms of depression and anxiety, as well as impairments in quality of life. To date, there is no mental health program specifically designed to address the mental health needs of children with CF. In the interest of informing the development of an accessible (i.e., Internet-delivered) mental health program, the present study examined the information and service needs of children with CF from the perspective of children with CF, their parents, and CF health care providers. Methods: A qualitative research design was used. Participants (n = 16) included children with CF (n = 5, Mage = 9.25, SD = 1.29), parents (n = 7, Mage = 36.43, SD = 3.46), and health care providers (n = 4, Mage = 44.00, SD = 10.46) recruited from regional CF clinics. Participants completed a brief demographic questionnaire. Semi-structured individual interviews were conducted with all participants. Results: Thematic content analysis generated four major themes: (1) challenges living with CF, (2) coping, (3) building independence, and (4) bridging gaps in services. Each theme was comprised of several subthemes. Conclusions: The findings highlight many emotional and social challenges experienced by children with CF and their families. Providing effective support for the entire family in managing and coping with CF was emphasized. Information gathered in the present study will be used, in combination with the empirical literature, to inform the development of an Internet-delivered mental health prevention program for children living with CF.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".