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Record W4309096927 · doi:10.1177/14713012221138145

Conceptualizing access to community-based supports from the perspectives of people living with young onset dementia, family members and providers

2022· article· en· W4309096927 on OpenAlexafffundabout
Sheila Novek, Verena Menec

Bibliographic record

VenueDementia · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
FundersAlzheimer Society of ManitobaCanadian Institutes of Health ResearchResearch Manitoba
KeywordsDementiaGerontologyPsychologyMedicineDisease

Abstract

fetched live from OpenAlex

People living with young onset dementia and their families have significant support needs, but experience difficulties accessing services. This study explored the process of accessing community-based services drawing on semi-structured interviews with people living with dementia, family members and providers in Winnipeg, Canada. Data analysis involved a combination of inductive coding and theoretical analysis using the candidacy framework as a conceptual lens. Forced to navigate services that do not recognize people with young onset dementia as a user group, participants experienced ongoing barriers that generated continuous work and stress for families. Access was constrained by information resources geared towards older adults and restrictive eligibility criteria that constructed people with young onset dementia as "not impaired enough" or "too impaired". At the organizational level, fragmentation and underrepresentation of young onset dementia diminished access. Our findings underscore the need for continuous, coordinated supports alongside broader representation of young onset dementia within research, policy, and practice. We conclude with a discussion of how the candidacy theory could be extended to account for the social and political status of user groups.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.019
GPT teacher head0.285
Teacher spread0.265 · 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 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

Citations10
Published2022
Admission routes3
Has abstractyes

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