The experience of children using long-term non-invasive ventilation: a qualitative study
Bibliographic record
Abstract
Objectives: To identify factors to optimize long-term non-invasive ventilation (LT-NIV) use by exploring the experience of children using LT-NIV and their parents. Study design and methods: A qualitative framework analysis method was used. Children aged 8-12 years who used LT-NIV for at least 3-months and their parents/guardians were approached to participate. Thematic analysis of data derived from focus group interviews, conducted separately for children and parents, was performed. Findings were coded and grouped into identified themes. Results: Data analysis identified four themes: (1) "The double-edged sword," which identified benefits and challenges of LT-NIV use; (2) "Feeling different," where children and parents described fears, frustrations, and concerns including emotional and social implications, and physical changes; (3) "It's not just about the mask," highlighted the influence of equipment issues, including the mask interface, headgear, tubing and humidity, and their impact on tolerance and use of LT-NIV; and (4) "Through the eyes of experience-children and parents as experts for change," which captured ideas for the functional and aesthetic improvement of the equipment including the need for pediatric specific technology. Conclusions: LT-NIV use has two sides; it helps to improve lives though requires an investment of time and commitment to ensure success. Investing in pediatric-specific equipment needs to be a priority as do alliances between healthcare providers, children who use LT-NIV, and their families. Future technology development and studies of adherence need to consider the experiences of children and their families to reduce the challenges and support optimal use of LT-NIV.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".