MétaCan
Menu
← Back to cohort
Record W4404697743 · doi:10.1186/s12913-024-11565-0

Formulating recommendations to improve care for persons living with dementia: deliberative dialogues with multiple stakeholders in the province of Quebec, Canada

2024· article· en· W4404697743 on OpenAlexafffundabout
Alexandra Lemay-Compagnat, Deniz Cetin‐Sahin, Laura Rojas‐Rozo, Geneviève Arsenault‐Lapierre, Yves Couturier, Howard Bergman, Isabelle Vedel

Bibliographic record

VenueBMC Health Services Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMcGill University Health CentreUniversité de SherbrookeCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsNursing researchHealth informaticsHealth administrationMedicinePublic healthDementiaQuality of Life ResearchNursingAssisted livingHealth services researchGerontology

Abstract

fetched live from OpenAlex

BACKGROUND: Persons living with dementia and their care partners encounter many challenges within the health and social care system, including lack of information, support, counselling, and access to community services, as well as significant staff turnover in home care services. The objective of this study was to work with multiple stakeholders to formulate relevant and feasible recommendations to improve care for persons living with dementia and their care partners in Quebec, Canada. METHODS: We conducted deliberative dialogues in the context of a large mixed methods study on the care of persons living with dementia and care partners. First, we organized two deliberative dialogues with care partners to formulate recommendations informed by the quantitative and qualitative results of the large study. These recommendations were further discussed in a third deliberative dialogue focused on the prioritization of relevant and feasible recommendations by clinicians, health project managers, and decision-makers. We performed a thematic analysis of the data using a multi-level framework: structural, organizational, provider, and patient perspectives. RESULTS: Participants formulated 14 recommendations. Two structural-level recommendations included fighting ageism and ensuring the same access to services in the whole province. Three organizational-level recommendations involved improving interdisciplinarity collaboration, improving access and follow-up in primary care, and adapting emergency departments. Additionally, two organizational-level recommendations were specific to healthcare crisis management (such as the COVID-19 pandemic): ensuring both the regular communication and the flexibility of implemented rules. Four provider-level recommendations encompassed providing more training on dementia, offering more training on levels of care, reviewing the relationship-based approach in training programs, and revising and optimizing medications. There were three patient-level recommendations including strengthening partnerships with persons living with dementia and care partners, guaranteeing personalized services and care, and reinforcing support for care partners. CONCLUSION: The deliberative dialogues enabled us to formulate relevant recommendations based on research evidence, the lived experience of care partners, and the expertise of clinicians, health project managers and decision-makers. The results revealed several recommendations that will help mitigate the challenges faced by persons living with dementia and care partners in the health and social care system by informing policies and practices.

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)0.027
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.993

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0410.012
Scholarly communication0.0070.003
Open science0.0030.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.101
GPT teacher head0.429
Teacher spread0.328 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueBMC Health Services Research→Same topicGeriatric Care and Nursing Homes→French-language works237,207→