Two-way Automated Text Messaging Support From Community Pharmacies for Medication Taking in Multiple Long-term Conditions: Human-Centered Design With Nominal Group Technique Development Study
Bibliographic record
Abstract
BACKGROUND: Reviews of digital communication technologies suggest that they can be effective in supporting medication use; however, their use alongside nondigital components is unclear. We also explored the delivery of a digital communication intervention in a relatively novel setting of community pharmacies and how such an intervention might be delivered to patients with multiple long-term conditions. This meant that despite the large number of intervention examples available in the literature, design questions remained, which we wanted to explore with key stakeholders. Examples of how to involve stakeholders in the design of complex health care interventions are lacking; however, human-centered design (HCD) has been suggested as a potential approach. OBJECTIVE: This study aimed to design a new community pharmacy text messaging intervention to support medication use for multiple long-term conditions, with patient and health care professional stakeholders in primary care. METHODS: HCD was used to map the intervention "journey" and identify design questions to explore with patients and health care professionals. Six prototypes were developed to communicate the intervention concept, and a modified version of the Nominal Group Technique was used to gather feedback. Nominal group meetings generated qualitative data using questions about the aspects that participants liked for each prototype and any suggested changes. The discussion was analyzed using a framework approach to transform feedback into statements. These statements were then ranked using a web-based questionnaire to establish a consensus about what elements of the design were valued by stakeholders and what changes to the design were most important. RESULTS: A total of 30 participants provided feedback on the intervention design concept over 5 nominal group meetings (21 health care professionals and 9 patients) with a 57% (17/30) response rate to the ranking questionnaire. Furthermore, 51 proposed changes in the intervention were generated from the framework analysis. Of these 51 changes, 27 (53%) were incorporated into the next design stage, focusing on changes that were ranked highest. These included suggestions for how text message content might be tailored, patient information materials, and the structure for pharmacist consultation. All aspects that the participants liked were retained in the future design and provided evidence that the proposed intervention concept had good acceptability. CONCLUSIONS: HCD incorporating the Nominal Group Technique is an appropriate and successful approach for obtaining feedback from key stakeholders as part of an iterative design process. This was particularly helpful for our intervention, which combined digital and nondigital components for delivery in the novel setting of a community pharmacy. This approach enabled the collection and prioritization of useful multiperspective feedback to inform further development and testing of our intervention. This model has the potential to minimize research waste by gathering feedback early in the complex intervention design process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".