Implementing a text message-based intervention to support type 2 diabetes medication adherence in primary care: a qualitative study with general practice staff
Bibliographic record
Abstract
BACKGROUND: The Support through Mobile Messaging and digital health Technology for Diabetes (SuMMiT-D) project has developed, and is evaluating, a mobile phone-based intervention delivering brief messages targeting identified behaviour change techniques promoting medication use to people with type 2 diabetes in general practice. The present study aimed to inform refinement and future implementation of the SuMMiT-D intervention by investigating general practice staff perceptions of how a text message-based intervention to support medication adherence should be implemented within current and future diabetes care. METHODS: Seven focus groups and five interviews were conducted with 46 general practice staff (including GPs, nurses, healthcare assistants, receptionists and linked pharmacists) with a potential role in the implementation of a text message-based intervention for people with type 2 diabetes. Interviews and focus groups were audio-recorded, transcribed and analysed using an inductive thematic analysis approach. RESULTS: Five themes were developed. One theme 'The potential of technology as a patient ally' described a need for diabetes support and the potential of technology to support medication use. Two themes outlined challenges to implementation, 'Limited resources and assigning responsibility' and 'Treating the patient; more than diabetes medication adherence'. The final two themes described recommendations to support implementation, 'Selling the intervention: what do general practice staff need to see?' and 'Fitting the mould; complementing current service delivery'. CONCLUSIONS: Staff see the potential for a text message-based support intervention to address unmet needs and to enhance care for people with diabetes. Digital interventions, such as SuMMiT-D, need to be compatible with existing systems, demonstrate measurable benefits, be incentivised and be quick and easy for staff to engage with. Interventions also need to be perceived to address general practice priorities, such as taking a holistic approach to care and having multi-cultural reach and relevance. Findings from this study are being combined with parallel work with people with type 2 diabetes to ensure stakeholder views inform further refinement and implementation of the SuMMiT-D intervention.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".