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Record W4395117603 · doi:10.1177/14713012241249796

Living with dementia: Exploring the intersections of culture, race, and dementia, stigma

2024· article· en· W4395117603 on OpenAlexafffund
Karen Lok Yi Wong, Granville B. Johnson, Deborah O’Connor

Bibliographic record

VenueDementia · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsDementiaStigma (botany)InvisibilityPsychologyFeelingQualitative researchRace (biology)RacismImmigrationExploratory researchDevelopmental psychologySocial psychologyMedicinePsychiatryGender studiesDiseaseSociology

Abstract

fetched live from OpenAlex

Research documents the presence of stigma and discrimination as key components in the lived experience of dementia. However, to date, there is limited understanding regarding how social location, particularly as it relates to culture and race, may shape this experience of stigma and discrimination. In this qualitative exploratory study, personal interviews were held with ten Chinese Canadians living with dementia focused on better understanding how culture, race, and dementia stigma influence their experiences. From the onset, themes related to stigma and discrimination were woven into the participants' stories about living with dementia. Consistent with other research, all participants described an increased sense of vulnerability and invisibility related to how both they and others responded to their diagnosis of dementia. Participants also provided examples of how this experience of stigma was compounded by culture, race, and immigration status. Importantly, these acts of stigma and discrimination were both externally and internally imposed, resulting in feelings of lack of safety and insecurity. This research draws attention to the increased vulnerability that accompanies a diagnosis of dementia and illustrates how this may be heightened by one's culture and racism.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.963

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)0.0010.000

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 head0.040
GPT teacher head0.329
Teacher spread0.289 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

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