Patient knowledge and pulmonary medication adherence in adult patients with cystic fibrosis
Bibliographic record
Abstract
Background and objectives: Patient knowledge of lung function (ie, forced expiratory volume in 1 s [FEV 1 ]% predicted) and the intended benefits of their prescribed pulmonary medications might play an important role in medication adherence, but this relationship has not been examined previously in patients with cystic fibrosis (CF). Methods: All patients diagnosed with CF and without prior lung transplantation were invited to complete knowledge and self-reported medication adherence questionnaires during routine outpatient visits to the Adult CF Clinic, St Paul’s Hospital, Vancouver, Canada from June 2013 to August 2014. Results: A total of 142 out of 167 (85%) consecutive adults attending CF clinic completed patient knowledge and medication adherence survey questionnaires. Sixty-four percent of the patients recalled their last FEV 1 % predicted value within 5%, and 70% knew the intended benefits of all their prescribed medications. Self-reported adherence rates were highest for inhaled antibiotics (81%), azithromycin (87%), and dornase alpha (76%) and lowest for hypertonic saline (47%). Individuals who knew their FEV 1 % predicted value within 5% were more likely to self-report adherence to dornase alpha (84% vs 62%, P =0.06) and inhaled antibiotics (88% vs 64%, P =0.06) compared to those who did not, but these associations were not statistically significant. There were no significant associations observed between patient knowledge of intended medication benefits and self-reported medication adherence. Conclusion: Contrary to our hypothesis, disease- and treatment-related knowledge was not associated with self-reported medication adherence. This suggests other barriers to medication adherence should be targeted in future studies aiming to improve medication adherence in adults with CF. Keywords: cystic fibrosis, health literacy, medication adherence, patient compliance, patient medication knowledge
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".