Clinician Perceptions of a Novel Multicomponent Digital Care Assistant and Support Program for People After Stroke or Transient Ischemic Attack (CAPS) for the Secondary Prevention of Stroke: Qualitative Study
Bibliographic record
Abstract
Background: We co-designed the novel multicomponent CAPS (Care Assistant and support Program for people after Stroke or transient ischemic attack) to augment the secondary prevention of stroke. Objective: Following completion of a feasibility study, we sought feedback from Australian clinicians and service provider representatives (the potential deliverers of CAPS) regarding their perceptions of CAPS for secondary prevention and the pathways to real-world adoption. Methods: This was a qualitative descriptive study of clinicians and service provider representatives involved in the delivery of stroke care around Australia. A pragmatic convenience sample was obtained by contacting previous CAPS co-design study participants; leveraging professional networks (eg, LinkedIn); and distributing study flyers and newsletters via primary health care networks, general practitioner (GP) networks, and social media posts (Commonwealth Scientific and Industrial Research Organization's LinkedIn pages). Semistructured interviews and focus groups were conducted virtually with clinicians and representatives of the Stroke Foundation (Australia). Qualitative content analysis was undertaken. Results: Overall, 18 clinicians and 3 Stroke Foundation representatives participated from 5 Australian states, including medical specialists, GPs, nurses, and allied health professionals. We collected their perceptions of CAPS, categorized as potential benefits of the program for secondary prevention, and the considerations for facilitators and challenges to real-world program implementation. The perceived benefits of supporting self-management for patients and facilitating informed decision-making for clinicians were identified. Discussions regarding program implementation included program initiation and duration, patient support considerations, and workflow alignment, which involved consideration of the barriers and enablers to uptake within primary care practice and Stroke Foundation outreach support programs. Conclusions: There was support from participants for the potential of CAPS to improve the secondary prevention of stroke. However, approaches for addressing the challenges raised by participants, including further implementation and integration considerations, such as sustainability of the model of care, are likely required for CAPS to be successfully embedded within clinical settings.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".