An Intervention Mapping Approach to Developing a Stroke Literacy Video for Recent Stroke Survivors: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Most vascular events after stroke may be prevented by modifying vascular risk factors through medical and behavioral interventions. Stroke literacy-an understanding of stroke symptoms, risk factors, and treatment-likely contributes to vascular risk factor control and in turn stroke recurrence risk. Stroke literacy is the lowest among adults belonging to racial and ethnic minority populations in the United States. Video-based interventions targeting stroke literacy may help acute stroke survivors understand stroke and subsequently reduce the risk of stroke recurrence. However, the failure of prior stroke literacy interventions may be due in part to the fact that the interventions were not theory-driven. Intervention mapping (IM) provides a framework for use in the development, implementation, and evaluation of evidence-informed, health-related interventions. OBJECTIVE: We aimed to develop a video-based educational intervention to improve stroke literacy in hospitalized patients with acute stroke. METHODS: The 6-step iterative process of IM was used to develop a video-based educational intervention and related implementation and evaluation plans. The six steps included a needs assessment, the identification of outcomes and change objectives, the selection of theory- and video-based intervention methods and practical applications, the development of a video-based stroke educational intervention, plans for implementation, and evaluation strategies. RESULTS: A 5-minute video-based educational intervention was developed. The IM approach led to successful intervention development by emphasizing stakeholder involvement, generation and adoption, and information retainment in the planning phase of the intervention. A planned approach to video adoption, implementation, and evaluation was also developed. CONCLUSIONS: An IM approach guided the development of a 5-minute video-based educational intervention to promote stroke literacy among acute stroke survivors. Future studies are needed to assess the use of technology and digital media to support widespread access and participation in video-based health literacy interventions for populations with acute and chronic stroke. Studies are needed to assess the impact of video-based educational interventions that are paired with stroke systems of care optimization to reduce the risk of stroke recurrence. Furthermore, studies on culturally and linguistically sensitive video-based stroke literacy interventions are needed to address known racial and ethnic disparities in stroke literacy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1371/journal.pone.0171952.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".