MétaCan
Menu
← Back to cohort
Record W4289878990 · doi:10.2196/31903

An Intervention Mapping Approach to Developing a Stroke Literacy Video for Recent Stroke Survivors: Development and Usability Study

2022· article· en· W4289878990 on OpenAlexvenueno aff
Mary Carter Denny, Andrea Ancer Leal, Tahani Casameni Montiel, Keona Wynne, Gabrielle Edquilang, Kim Yen Thi Vu, Farhaan Vahidy, Sean I. Savitz, Jennifer E. S. Beauchamp, Anjail Sharrief

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of Health
KeywordsPsychological interventionStroke (engine)Intervention (counseling)UsabilityLiteracyMedicineIntervention mappingPhysical therapyPsychologyNursingPublic healthComputer scienceHealth promotion

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.098
GPT teacher head0.424
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Formative Research→Same topicAcute Ischemic Stroke Management→French-language works237,207→