MétaCan
Menu
Back to cohort
Record W3180586814 · doi:10.1017/s0714980821000192

Stigma Reduction Interventions of Dementia: A Scoping Review

2021· review· en· W3180586814 on OpenAlexaff
Juanita-Dawne Bacsu, Shanthi Johnson, Megan E. O’Connell, Marc Viger, Nazeem Muhajarine, P. A. Hackett, Bonnie Jeffery, Nuelle Novik, Thomas McIntosh

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2021
Typereview
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of AlbertaUniversity of SaskatchewanSaskatchewan HealthUniversity of Regina
Fundersnot available
KeywordsDementiaPsychological interventionStigma (botany)PsychologyInclusion (mineral)Quality of life (healthcare)NursingGerontologyMedicineMedical educationPsychiatrySocial psychologyDisease

Abstract

fetched live from OpenAlex

Despite its global importance and the recognition of dementia as an international public health priority, interventions to reduce stigma of dementia are a relatively new and emerging field. The purpose of this review was to synthesize the existing literature and identify key components of interventions to reduce stigma of dementia. We followed Arksey and O'Malley's scoping review process to examine peer-reviewed literature of interventions to reduce dementia-related stigma. A stigma-reduction framework was used for classifying the interventions: education (dispel myths with facts), contact (interact with people with dementia), mixed (education and contact), and protest (challenge negative attitudes). From the initial 732 references, 21 studies were identified for inclusion. We found a variety of education, contact, and mixed interventions ranging from culturally tailored films to intergenerational choirs. Findings from our review can inform the development of interventions to support policies, programs, and practices to reduce stigma and improve the quality of life for people with dementia.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.851
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.074
GPT teacher head0.386
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.

Study designOther design
Domainnot available
GenreReview

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

Citations64
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicMental Health Treatment and AccessFrench-language works237,207