IMPROVING THE HEALTH BEHAVIOURS OF COPD PATIENTS: IS HEALTH LITERACY THE ANSWER?
Bibliographic record
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality (Vogelmeier et al., 2017). Adherence to prescribed medications and adequate medication inhalation technique (MIT) is critical for optimal management of COPD, as is the proper use of the medication delivery device.O’Conor et al. (2019) found that lower health literacy (HL) was associated with both poor medication adherenceand MIT. HL, according to the Process-Knowledge Model, consists of both processing capacity and knowledge (Chin et al., 2017). COPD most commonly occurs in older adults (Cazzola, Donner, & Hanania, 2007). Older adults tend to have lower processing capacity (Chin et al., 2017). The purpose of this study was to determine if HL was associated with medication refill adherence (MRA)and/or MIT. Fifty-seven participants completed a questionnaire package that included demographic questions, measures of HL, and assessments of MRA and MIT. A subset of twenty patients participated in qualitative interviews. Results indicated that lower HL was associated with both lower MRA and poor MIT, and qualitative findings revealed the need for further information. Future research should focus on testing educational materials that have been designed and/or reformatted to meet the lower processing capacity of older adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".