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Record W4403000986 · doi:10.1111/opn.12651

The Experience of Hospitalisation for People Living With Dementia: A Qualitative Exploration of How Context Shapes Experiences

2024· article· en· W4403000986 on OpenAlexafffund
Elaine Moody, Gianisa Adisaputri, Heather McDougall, Lori E. Weeks, Anne C. Belliveau, Patricia Bilski, Kata Koller

Bibliographic record

VenueInternational Journal of Older People Nursing · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsNova Scotia Health AuthorityDalhousie University
FundersAlzheimer Society
KeywordsDementiaContext (archaeology)DyadActivities of daily livingPsychological interventionPsychologyQualitative researchMedicineGerontologyNursingPsychiatryDevelopmental psychologySociologyDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: Dementia has become an increasingly pressing issue in healthcare systems, and acute care settings such as hospitals have been shown to be particularly problematic for people living with dementia. People living with dementia are hospitalised at higher rates than those without and often have poor experiences and outcomes of hospitalisation. To support the evolution of hospital systems to better meet the needs of people living with dementia, it is important to consider the relationship between the context of the hospital and the experiences of people living with dementia and their caregivers. The term context refers to the environment and conditions that shape experiences and includes elements such as policies, procedures, design and social norms. A greater understanding of the relationship between the hospital context and the experiences of people living with dementia will support the future development of interventions to modify contextual elements to improve the experiences of people living with dementia. Our aim was to explore the relationship between the experiences of people living with dementia and their caregivers in hospital, and the contextual elements of the hospital. METHODS: In this interpretive descriptive study, we conducted interviews with people living with dementia and caregivers of people living with dementia about their experiences in hospital. Interviews were conducted with people living with dementia (n = 3), caregivers of people living with dementia (n = 8) and one dyad of a person living with dementia and their caregiver (n = 1) about the hospitalisation of the person living with dementia. Using inductive and deductive methods of analysis, we describe contextual factors that shape their experiences. RESULTS: Our findings demonstrate how the hospital context shaped the experiences of people living with dementia and their caregivers while the person living with dementia was hospitalised. The themes reflect elements of the hospital context: communication systems, the value of caregivers, the focus on acute physical needs, staff capacity, the physical environment and normalised practices. The findings suggest that there are aspects of the hospital context that can be modified to influence the experiences of people living with dementia and their caregivers. CONCLUSION: This study demonstrates how the context of hospital shapes the experiences of people living with dementia and their caregivers and points to aspects of the context that health system leaders could explore to begin to improve the capacity of hospitals to support people living with dementia. IMPLICATIONS FOR PRACTICE: The study provides evidence of the need for health system leaders and practitioner to explore how the hospital context shapes the experiences of people wtih dementia and their caregivers, and begins to explore ways that contextual changes can improve their experiences.

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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)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0120.013
Scholarly communication0.0060.006
Open science0.0020.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.429
Teacher spread0.372 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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