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Record W7117272128 · doi:10.1002/alz70857_096634

Gender‐Related Facilitators and Barriers to Participation in Observational Neurocognitive Aging Research in Older Adults: A Fuzzy Cognitive Mapping Approach

2025· article· en· W7117272128 on OpenAlexaff
Vasvi Dhir, Isabel McDonald, Maude Gelinas Faucher, Ivan Sarmiento, Annick Gauthier, Neil Andersson, M J Yaffe, Maiya R. Geddes

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsMontreal Neurological Institute and HospitalSt Mary's Hospital CentreMcGill University
Fundersnot available
KeywordsNeurocognitiveObservational studyCognitionFuzzy cognitive mapCognitive agingCognitive mapDiversity (politics)

Abstract

fetched live from OpenAlex

BACKGROUND: Gender selection bias is a form of selection bias rooted in gender insensitivity or androcentrism, erroneously regarding research participants with different gender identities as similar/different. Neurocognitive aging research is largely subject to this bias, whereby study samples often overrepresent White, well-educated, women living in less socioeconomically deprived areas. This biased representation raises cardinal concerns about health equity and discrimination and about generalizability and reproducibility of neuroscientific findings. OBJECTIVES: This study identified gender-related barriers and facilitating factors older adults perceive when considering participation in observational neurocognitive aging research, as these factors may underly the bias. METHOD: We employed a participatory research methodology, called fuzzy cognitive mapping (FCM). The method is rooted in graph theory and social network analysis and portrays perspectives on what contributes to the occurrence of an outcome. Researchers discussed with participants from diverse groups and recorded their thoughts on what they perceived as factors encouraging or hindering their involvement in research. The factors were standardized across maps and organized in a causal network. We computed the fuzzy transitive closure (FTC) to quantify the maximum influence each factor has on other factors through direct and indirect links. The factors were aggregated into categories through inductive analysis. We computed measures of centrality, whereby categories with higher outdegree centrality scores were perceived as causes in the causal network and those with higher indegree centrality were perceived as outcomes. We operated analyses in a gender-segregated manner. RESULTS: We co-created 24 maps with participants (12 women, 12 men). Our results show the shared and distinct factors older men and women perceive when considering participation in brain health research. Both men and women perceived individuals' personal traits, quality of participant-researcher communications, logistic considerations, and research-specific practices as influential. Only women perceived their individual experiences related to the research as facilitating research engagement, whereas willingness to return benefits to the general population through research was exclusive to men. CONCLUSION: This study addressed the lack of diversity and poor representativeness, particularly of older men in neurocognitive aging research samples, to generate meaningful sampling strategies and ultimately knowledge suited to generalizable application, thus advancing toward unbiased science.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.095
GPT teacher head0.354
Teacher spread0.259 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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