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Record W4388428513 · doi:10.1177/23743735231211066

The Social Construction of Dementia: Implications for Healthcare Experiences of Caregivers and People Living with Dementia

2023· article· en· W4388428513 on OpenAlexafffund
Nusrat Farhana, Allie Peckham, Husayn Marani, Monika Roerig, Gregory P. Marchildon

Bibliographic record

VenueJournal of Patient Experience · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchAlzheimer Society
KeywordsDementiaThematic analysisGerontologyHealth carePsychologyQualitative researchDiseaseSocial supportMedicineNursingSocial psychologySociology

Abstract

fetched live from OpenAlex

Globally, systems have invested in a variety of dementia care programs in response to the aging population and those who have been diagnosed with dementia. This study is a qualitative secondary analysis of interview data from a larger study investigating stakeholder perceptions of programs that support caregivers and people living with an Alzheimer's Disease or Alzheimer's Disease-related dementia (AD/ADRD) in five North American jurisdictions. This study analyzed interviews with individuals living with an AD/ADRD and caregivers of individuals living with an AD/ADRD (n = 11). Thematic analysis was conducted to understand how the perception of dementia may have shaped their engagement and experience with healthcare systems. Our analysis resulted in three main themes of care users' experience: (i) undesirable experience owing to the overarching negative shared understanding and stereotyping of dementia; (ii) dismissal throughout disease progression when seeking health and social care support; and (iii) dehumanization during care interactions. The findings carry critical social and clinical implications, for example, in informing person-centered approaches to care, and communication tools clinicians can use to enhance provider, patient, and caregiver well-being.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.343
Teacher spread0.322 · 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 designObservational
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

Citations11
Published2023
Admission routes2
Has abstractyes

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