Bridging the gap: Key informants’ perspectives on patient barriers in asthma and COPD self-management and possible solutions
Bibliographic record
Abstract
RATIONALE: Despite the importance of self-management for asthma/chronic obstructive pulmonary disease (COPD), there is a lack of information on health care professionals’ (HCPs) perspectives of patient barriers to self-management and the possible solutions to overcome such barriers.OBJECTIVES: To assess key informants’ (HCPs, researchers, and policymakers) perspectives on the barriers that they perceived an asthma/COPD patient may be faced with and the possible solutions to address them.METHODS: Between December 2015 and April 2016, 57 potential key informants from across the globe were invited to participate in in-depth interviews. Questions included: the skills a patient would need to manage their asthma/COPD; barriers inhibiting successful self-management practices; and the actions taken to address these barriers. The data was transcribed verbatim and analyzed using thematic analysis.RESULTS: Interviews were conducted with 45 (15 male) key informants from Canada, the United States of America, the United Kingdom, and Australia. Perceived barriers to self-management included: information overload; inconsistent information; time constraints; medical jargon and reading level of materials; beliefs and attitudes about treatment; lack of patient involvement in developing materials; and memory problems and age. Six solutions were suggested: take-home materials; tailoring education; follow-up visits; promotion of questions; better communication and building relationships; and teach-back method.CONCLUSIONS: Our findings show that improvements are needed in terms of the interactions and relationships between patients and HCPs in order to fully engage patients in the use of self-management practices. Patient involvement in the development of educational materials is a key factor to ensure the applicability of health information and promote uptake.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".