“If You Have CF, I Will Eat My Shoe”: Lived experiences and needs of individuals with adult-diagnosed cystic fibrosis
Bibliographic record
Abstract
RATIONALE: Cystic Fibrosis (CF) is a genetic, multisystem diseaseCitation1 that can be diagnosed in both childhood and adulthood. Diagnosis of CF during childhood is well-documented in the literature, and there is substantial information available for the parents of children with CF.Citation2 However, there is less known about the experiences of those with adult-diagnosed CF coupled with limited to no tailored resources for those who receive a later CF diagnosis.Citation3,Citation4OBJECTIVES The current study examined the lived experiences and information needs of individuals with adult-diagnosed CF in Canada, as well as attitudes toward receiving CF-related education via the Internet.METHODS Eight individuals diagnosed with CF as adults (Mage = 41.71, SD = 14.47) and 9 health care providers (HCP; Mage = 45.35, SD = 7.91) completed a brief demographic questionnaire, consent form, and individual semi-structured interview either via Zoom or telephone. Thematic analysis was used to analyze the data collected for each participant group.MAIN RESULTS: Five major themes were generated from both participant groups: (1) CF information needs; (2) challenges; (3) emotions and coping; (4) health service needs; and (5) Internet-delivered resource considerations.CONCLUSIONS The findings highlight the unique experiences and challenges faced by those diagnosed with CF in adulthood. Information from the current study will be used alongside the empirical literature to inform the development of an evidence-based, Internet-delivered resource for this population.
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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 | 0.004 | 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 | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".