Managing Medications During “Sick Days” in Patients With Diabetes, Kidney, and Cardiovascular Conditions: A Theory-informed Approach to Intervention Design and Implementation
Bibliographic record
Abstract
OBJECTIVES: Our aim in this work was to 1) explore barriers and enablers to patient and health-care provider (HCP) behaviours related to sick-day medication guidance (SDMG), 2) identify theory-informed strategies to advise SDMG intervention design, and 3) obtain perspectives on an eHealth tool for this purpose. METHODS: A qualitative descriptive study using qualitative conventional content analysis was undertaken. Interviews and focus groups were held with patients and HCPs from January 2021 to April 2022. Data were analyzed using the Behaviour Change Wheel and Theoretical Domains Framework to inform intervention design. RESULTS: Forty-eight people (20 patients, 13 pharmacists, 12 family physicians, and 3 nurse practitioners) participated in this study. Three interventions were designed to address the identified barriers and enablers: 1) prescriptions provided by a community-based care provider, 2) pharmacists adding a label to at-risk medications, and 3) built-in prompts for prescribing and dispensing software. Most participants accepted the concept of an eHealth tool and identified pharmacists as the ideal point-of-care provider. Challenges for an eHealth tool were raised, including credibility, privacy of data, medical liability, clinician remuneration and workload impact, and equitable access to use of the tool. CONCLUSIONS: Patients and HCPs endorsed non-technology and eHealth innovations as strategies to aid in the delivery of SDMG. These findings can guide the design of future theory-informed SDMG interventions.
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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 | 0.056 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".