Asset Literacy Following Stroke: Implications for Disaster Resilience
Bibliographic record
Abstract
OBJECTIVE: The World Stroke Organization "1 in 6" campaign aims to raise awareness that 1 in 6 persons will experience a stroke during their lifetime. With aging populations and improved survival rates, an increased number of survivors live with functional limitations and require supportive care. This has important implications for implementing an all-of-society approach to disaster risk reduction. In this study, we explore the assets that stroke survivors and caregivers consider useful in supporting their capacity to manage routine activities and independent living and to respond to a disaster. METHODS: Transcripts from interviews with stroke survivors and caregivers were analyzed by use of content analysis. RESULTS: Assets were categorized into 4 classes: social, physical, energy, and personal characteristics and are presented as a household map. Emergent themes suggested that understanding how to mobilize assets is complicated yet essential for building resilience. Household resilience requires people have self-efficacy and motivation to move from awareness to action. The findings informed development of a conceptual model of asset literacy and household resilience following stroke. CONCLUSIONS: Interventions to enhance asset literacy can support an all-of-society approach to disaster risk reduction through awareness, empowerment, participation, innovation, and engagement. (Disaster Med Public Health Preparedness. 2018; 12: 312-320).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".