“In my age, we didn’t have the computers”: Using a complexity lens to understand uptake of diabetes eHealth innovations into primary care—A qualitative study
Bibliographic record
Abstract
BACKGROUND: Shared decision-making is a central component of person-centred care and can be facilitated with the use of patient decision aids (PtDA). Barriers and facilitators to shared decision-making and PtDA use have been identified, yet integration of PtDAs into clinical care is limited. We sought to understand why, using the concepts of complexity science. METHODS: We conducted 60-minute in-depth interviews with patients with diabetes, primary care physicians, nurses and dietitians who had participated in a randomized controlled trial examining the impact of MyDiabetesPlan (an online goal-setting PtDA). Relying on a qualitative description approach, we used a semi-structured interview guide to explore participants' experiences with using MyDiabetesPlan and how it was integrated into the clinical encounter and clinical care. Audiotapes were transcribed verbatim, then coded independently by two analysts. FINDINGS: 17 interviews were conducted (5 physicians, 3 nurses, 2 dietitians, 7 patients). Two themes were developed: (1) MyDiabetesPlan appeared to empower patients by providing tailored patient-important information which engaged them in decision-making and self-care. Patients' use of MyDiabetesPlan was however impacted by their competing medical conditions, other life priorities and socioeconomic context. (2) MyDiabetesPlan emphasized to clinicians a patient-centred approach that helped patients assume greater ownership for their care. Clinicians' use of MyDiabetesPlan was impacted by pre-existing clinical tools/workplans, workflow, technical issues, clinic administrative logistics and support, and time. How clinicians adapted to these barriers influenced the degree to which MyDiabetesPlan was integrated into care. CONCLUSIONS: A complexity lens (that considers relationships between multiple components of a complex system) may yield additional insights to optimize integration of PtDA into clinical care. A complexity lens recognizes that shared decision-making does not occur in the vacuum of a clinical dyad (patient and clinician), and will enable us to develop a family of interventions that address the whole process, rather than individual components. TRIAL REGISTRATION: ClinicalTrials.gov NCT02379078.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".