How patients’ representations of cystic fibrosis-related diabetes inform their health behaviours
Bibliographic record
Abstract
BACKGROUND: Although diabetes is a frequent complication of cystic fibrosis (CF), patients' behaviours tend not to comply with best practice recommendations. Using Leventhal's Common-Sense Model, we address this issue by exploring patients' representations of CF-related diabetes (CFRD) to better understand the discrepancy between patients' expected and observed health behaviours. METHODS: Semi-structured individual interviews were conducted with patients (n = 39) in six CF clinics in Quebec, Canada. These interviews were part of a larger research project on screening and management practices for CFRD. RESULTS: Illness representations differed between two groups of interviewed patients: (1) one group had either CF without dysglycemia or CF with impaired glucose tolerance; and (2) the other group had CFRD. Both representations were internally consistent and encompassed Leventhal's five dimensions of illness representation: illness identity, cause, timeline, consequences and control. CONCLUSIONS: Patients require specific information on CFRD. The screening phase could be a crucial time to help patients adjust their representations to fit the reality of CFRD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".