Working to Have a Normal Life With Cystic Fibrosis in an Adherence-Driven Health Care System
Bibliographic record
Abstract
BACKGROUND: Adults with cystic fibrosis (CF) must continuously manage their condition, while working for a living, and want a normal life. Adherence rates to treatments/medications are less than optimal. Existing theory offers little to explain adherence rates. The purpose of this study was to develop a theory to further the understanding of how people with CF manage their condition in an adherence-driven health care system. METHODS: Constructivist Grounded Theory methodology was used to conduct 27 semistructured interviews with adults with CF, family members, and health care providers. Data collection and analysis were simultaneous, using constant comparative methods, initial and focused coding, and category identification and reduction to develop a theory. RESULTS: Doing what works to balance life and CF is the theory generated from this study. The main concern of participants was to be seen as normal. The theory depicts what participants with CF and their family members do about their concerns and involves 4 interrelated processes: working overtime, receiving support, passing as normal, and facing disease progression. CONCLUSION: Participants did not relate to the term nonadherent; rather they described working overtime to manage CF, to work, and to have a normal life. Health care provider and researcher perspectives on adherence differ from those of people with CF. Engaging adults with CF and health care providers in a dialogue in which expectations are shared may lead to individualized treatment regimens that work, because adults with CF will do what works.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".