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Record W4402993203 · doi:10.2196/58895

Disaster Preparedness Intervention for Older Adults (Seniors’ Positive Involvement in Community Emergencies): Protocol for a Quasi-Experimental Study

2024· article· en· W4402993203 on OpenAlexvenueno aff
Sharon White, Joseph S. Lightner, Julia Crowley, Amanda Grimes, Kathleen Spears, Steven R. Chesnut

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintIntervention (counseling)PreparednessGerontologyProtocol (science)Suicide preventionMedical emergencyPoison controlPsychologyMedicineDisaster preparednessEmergency managementNursingComputer scienceWorld Wide WebAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Older adults comprise a substantial proportion of the US population requiring support during disaster events. Previous research demonstrates that older adults are resilient but deficient in disaster preparedness and lacking in community engagement. There is a gap in high-quality research in this area. OBJECTIVE: This study aims to fill this gap by developing a 4-phase intervention to improve mobility and balance, decrease fall risks (mitigation), increase knowledge of disaster preparedness (preparedness), improve community emergency operation plans (response), and improve self-efficacy in disaster recovery (recovery) for older adults. METHODS: This is a community-based, 10-month study in a large Midwestern urban and suburban location targeting community-dwelling older adults. The 4 phases of interventions address mitigation, preparedness, response, and recovery-aspects improving outcomes from disaster events. In total, 4 to 6 one-hour seminars each month are provided to community-dwelling older adults to improve disaster preparedness and recovery planning. A critical incident packet with resources on essential information such as medications, a communication plan, evacuation resources, and supplies was started and is being reviewed. Preintervention surveys are orally given, with research assistants aiding in any difficulties the participants have. After the surveys, 2 individual 20-minute presentations separated by a short break for snacks and initial completion of their disaster plan preserve the older adult's attention. Mitigation efforts to improve mobility and safety are offered with 10 visits to the older adults' residences, adapting physical activity and balance exercises to the individual's needs. To address response needs, the emergency operations plans for 2 of the major cities are being amended for specific functional needs and access guidelines. Measurements include accelerometers to assess improvement in mobility, fall risk assessments, an abbreviated Federal Emergency Management Association Household Survey, an assessment for disaster engagement with partners tool, a brief pain inventory assessment, and the General Self-Efficacy Scale. We analyze data descriptively and compare pre- and postintervention data for each phase with paired-samples t test and other nonparametric techniques (proportion tests and Wilcoxon signed-rank tests). Overarching objectives prioritized during this intervention include underscoring respect for the experience and resilience found in older adults and engaging them in specialized roles to support their communities during disaster events. RESULTS: The intervention was funded in July 2023; enrollment began in November 2023 and is continuing. We will conclude data collection by July 2025. Published study results can be expected in early 2025. CONCLUSIONS: With improved disaster preparedness, mobility, recovery planning, and inclusion as a resource in community disasters, older adults are expected to be safer and be able to age in place. If successful, future studies will focus on outreach and sustainability. This study will serve as a model for older adult disaster preparedness and community involvement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58895.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.115
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.221
GPT teacher head0.585
Teacher spread0.364 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreProtocol

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

Citations2
Published2024
Admission routes1
Has abstractyes

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