Exploring the role of competing demands and routines during the implementation of a self-management tool for type 2 diabetes: a theory-based qualitative interview study
Bibliographic record
Abstract
BACKGROUND: The implementation of new medical interventions into routine care involves healthcare professionals adopting new clinical behaviours and changing existing ones. Whilst theory-based approaches can help understand healthcare professionals' behaviours, such approaches often focus on a single behaviour and conceptualise its performance in terms of an underlying reflective process. Such approaches fail to consider the impact of non-reflective influences (e.g. habit and automaticity) and how the myriad of competing demands for their time may influence uptake. The current study aimed to apply a dual process theoretical approach to account for reflective and automatic determinants of healthcare professional behaviour while integrating a multiple behaviour approach to understanding the implementation and use of a new self-management tool by healthcare professionals in the context of diabetes care. METHODS: Following Diabetes UK's national release of the 'Information Prescription' (DUK IP; a self-management tool targeting the management of cholesterol, blood pressure and HbA1c) in January 2015, we conducted semi-structured interviews with 13 healthcare professionals (general practitioners and nurses) who had started to use the DUK IP during consultations to provide self-management advice to people with type 2 diabetes. A theory-based topic guide included pre-specified constructs from a previously developed logic model. We elicited healthcare professionals' views on reflective processes (outcome expectations, self-efficacy, intention, action and coping planning), automatic processes (habit), and multiple behaviour processes (goal priority, goal conflict and goal facilitation). All interviews were audio recorded and transcribed verbatim and all transcripts were independently double coded and analysed using content analysis. RESULTS: The majority of healthcare professionals interviewed reported strong intentions to use the DUK IP and having formed a habit of using them after a minimum of one month continuous use. Pop-up cues in the electronic patient records were perceived to facilitate the use of the tool. Factors that conflicted with the use of the DUK IP included existing pathways of providing self-management advice. CONCLUSION: Data suggests that constructs from dual process and multiple behaviour approaches are useful to provide supplemental understanding of the implementation of new self-management tools such as the DUK IP and may help to advance behavioural approaches to implementation science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".