Barriers and Enablers to Using a Patient-Facing Electronic Questionnaire: A Qualitative Theoretical Domains Framework Analysis
Bibliographic record
Abstract
BACKGROUND: Electronic patient questionnaires are becoming ubiquitous in health care. To address care gaps that contribute to poor asthma management, we developed the Electronic Asthma Management System, which includes a previsit electronic patient questionnaire linked to a computerized clinical decision support system. OBJECTIVE: This study aims to identify the determinants (barriers and enablers) of patient uptake and completion of a previsit mobile health questionnaire. METHODS: We conducted semistructured interviews with adult patients with asthma in Toronto, Canada. After demonstrating the questionnaire, participants completed the questionnaire using their smartphones and were then interviewed regarding perceived barriers and enablers to using and completing the questionnaire. Interview questions were based on the Theoretical Domains Framework to identify the determinants of health-related behavior. We generated themes that addressed the enablers and barriers to the uptake and completion of the questionnaire. RESULTS: In total, 12 participants were interviewed for saturation. Key enablers were as follows: the questionnaire was easy to complete without additional knowledge or skills and was perceived as a priority and responsibility for patients, use could lead to more efficient and personalized care, completion on one's own time would be convenient, and uptake and completion could be optimized through patient reminders. Concerns about data security, the usefulness of questionnaire data, the stress of completing it accurately and on time, competing priorities, and preferences to complete the questionnaire on other devices were the main barriers. CONCLUSIONS: The barriers and enablers identified by patients should be addressed by developing implementation strategies to enhance e-questionnaire use and completion by patients. As the use of e-questionnaires grows, our findings will contribute to implementation efforts across settings and diseases.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".