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Record W4402903737 · doi:10.2196/59306

Developing a Life Story Intervention for Older Adults With Dementia or at Risk of Delirium Who Were Hospitalized: Multistage, Stakeholder-Engaged Co-Design Study

2024· article· en· W4402903737 on OpenAlexvenueno aff
Sarah Flessa, James D. Harrison, Roniela Turnigan, Megan Rathfon, Jay Newton-Small, Stephanie Rogers

Bibliographic record

VenueJMIR Aging · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute on AgingNational Institutes of Health
KeywordsDeliriumDementiaPsychological interventionStorytellingStakeholderIntervention (counseling)MedicineNursingAcute careQualitative researchHealth carePsychologyPsychiatryDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Older adults with chronic or acute cognitive impairment, such as dementia or delirium, who are hospitalized face unique barriers to person-centered care and a higher risk for negative outcomes stemming from hospitalizations. There is a need for co-designed interventions adapted for these patients to the hospital setting to improve care and outcomes. Patient life storytelling interventions have demonstrated promise in enhancing person-centered care by improving patient-care team relationships and providing information to enable care tailored to individual needs and values. OBJECTIVE: This study aims to engage patients, care partners, and clinical stakeholders in a co-design process to adapt an existing life storytelling model for use with older adults with dementia and at risk of delirium in the acute care hospital setting. METHODS: We recruited patients with dementia or at risk of delirium who were hospitalized, their care partners, clinicians, and informaticists. A 3-stage co-design process that used a mixed methods data collection approach including in-depth interviews and surveys was completed. We used content analysis to analyze qualitative data and descriptive statistics to summarize quantitative data. RESULTS: In total, 27 stakeholder informants (ie, patients, care partners, and interdisciplinary care team [IDT] members) participated. Stakeholders were unanimously interested in using patient life stories as a tool for hospital care through electronic health record (EHR) integration. Stakeholders shared potential topics for life stories to cover, including social support, information on patients' key life events, and favorite activities. Participants provided insights into the logistics of integrating life stories into acute care, including interview arrangement, story-sharing methods, and barriers and facilitators. IDT members shared preferences on EHR integration, resulting in 3 co-designed mock-ups of EHR integration options. Stakeholders shared ways to optimize future acceptability and uptake, including engaging with the care team and promoting awareness of life stories, ensuring suitability to the acute environment (eg, distilling information in an easily digestible way), and addressing concerns for patient capacity and privacy (eg, engaging care partners when appropriate). Thoughts on potential impacts of life stories were also elicited, including improving patient- and care partner-IDT member relationships; humanizing patients; increasing clinical team, patient, and caregiver satisfaction; and enabling more specific, tailored care for patients with dementia and at risk of delirium. CONCLUSIONS: This study resulted in a co-designed life storytelling intervention for patients with dementia and at risk for delirium in an acute care hospital setting. Stakeholders provided valuable information to ensure future intervention acceptability and uptake, including potential benefits, facilitators, and challenges in the acute care setting.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.400
Teacher spread0.311 · 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
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

Citations0
Published2024
Admission routes1
Has abstractyes

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