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Record W7116843593 · doi:10.1002/alz70860_104349

Understanding the challenges faced by dementia researchers: Findings from a global survey on capacity building

2025· article· en· W7116843593 on OpenAlexaff
Florentina Morello García, Nicolas Corvalán, Micaela Maria Arruabarrena, María Florencia Clarens, Greta Keller, L. De Los Santos, M. Saint Martin, Ricardo Allegri, Cristiano Schaffer Aguzzoli, Lívia Amaral, Carolina Agata Ardohain Cristalli, Bruna Bellaver, Merci N Best, Madeleine Bloomquist, Igor C. Fontana, Kevin Chen, Neha Dubey, Cynthia Felix, Diego Fernández Slezak, Indira García‐Cordero, Micaela A Hernández, Ozama Ismail, Florence Johnson, Arshia Khan, Suélyn Koerich, Maria Celeste López Moreno, Pamela C.L. Ferreira, Nahuel Magrath Guimet, Marina Scop Madeiros, Markley Silva Oliveira, Guilherme Povala, Andreia Rocha, Matheus Scarpatto Rodrigues, Emma Patrice Ruppert, Kaitlin Seibert, Carolina Soares, Ezequiel Surace, Hannah Wilks, Tharick A. Pascoal, Jorge J. Llibre‐Guerra, Luc¡a Crivelli

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
Fundersnot available
KeywordsDementiaClosing (real estate)Psychological interventionVisibilityFoundation (evidence)Capacity buildingProfessional development

Abstract

fetched live from OpenAlex

BACKGROUND: Different global initiatives aim to support researchers in their fields of study. World Young Leaders in Dementia (WYLD) is an international organization dedicated to connecting and supporting young professionals in the dementia field. One of its missions is to promote the development of leadership skills through capacity building. This concept encompasses five key areas: education and training, funding and investment, infrastructure development, collaborations and networking, and community engagement. Understanding researchers' working and academic conditions is the first step toward implementing targeted interventions to foster their growth. The aim of this work is to describe the main challenges faced by dementia researchers globally. METHOD: A comprehensive survey was designed to gather insights from dementia researchers, exploring demographic information and five key domains of capacity building. It was disseminated through WYLD's membership list and partnerships with dementia-focused organizations (e.g., ISTAART Professional Interest Areas, International Neuropsychological Society Special Interest Groups). A preliminary analysis was performed, focusing on education and training as well as funding and investment. RESULT: The participants (n = 125) were primarily women (69%) residing in low- and middle-income countries (75.2%). Among respondents, only 39% hold full-time academic positions, and just 20% dedicate 100% of their professional time to academia. A 65% report that the scientific system in their country is either underdeveloped or not prioritized as public policy. Furthermore, 93.7% state that funding sources-including salaries, grants, travel scholarships, and other forms of support-are scarce or nonexistent for early-career researchers. However, 62.4% reported receiving specialized training in dementia, and 65.6% stated their groups provide regular learning opportunities. Finally, the primary barriers to leadership development were: limited financial resources, limited infrastructure, and low visibility and recognition. Figure 1 summarizes the main results. CONCLUSION: The findings underscore significant challenges faced by dementia researchers. Limited financial resources, inadequate infrastructure, and low visibility and recognition remain critical barriers to leadership development and professional growth. Nevertheless, many researchers reported access to specialized training and regular learning opportunities within their groups, providing a foundation for strengthening capacity-building initiatives. Closing these gaps through targeted interventions is crucial to fostering the next generation of leaders in dementia research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0010.006
Research integrity0.0010.002
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 head0.231
GPT teacher head0.379
Teacher spread0.148 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainIncentives
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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