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Record W4285361920 · doi:10.46692/9781447349013.010

Building community capacity for dementia in Canada: new directions in new places

2021· other· en· W4285361920 on OpenAlexaboutno aff
Alison Phinney, Eric Macnaughton, Elaine Wiersma

Bibliographic record

Venuenot available
Typeother
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaGeographyMedicineDisease

Abstract

fetched live from OpenAlex

Introduction Building Capacity for Meaningful Participation by People Living with Dementia is being undertaken in Canada under the umbrella of the federally funded Dementia Community Investment. This four-year Building Capacity Project (2019–23) models a bottom-up cross-sectoral approach to building and connecting community-based activities that provide meaningful opportunities for people with dementia to remain active and socially connected. In this chapter we examine how the project is designed to build practical knowledge from the ‘ground up’, working closely with people who are most directly affected by the issues (especially those with lived experience of dementia), and taking local context into account using methods of asset-based community development and developmental evaluation. Most importantly, this work is being carried out in two very different places, which is allowing us to both leverage our unique strengths and discover the common principles underlying successful approaches that can then be scaled up more broadly, including nationally. Ultimately, the aim is to create and share new knowledge about how this kind of ‘grass roots’ approach can lead to sustainable change at individual, community and institutional levels to promote social inclusion, raise awareness and reduce stigma around dementia. Recognising the importance of place in this project, we begin by describing where the work is happening. This allows us to make explicit the significance of local contexts, be they geographic, social, economic, cultural or political, which are intrinsic to how we are developing and evaluating the various activities and programmes. We then lay out the principles and evidence that are guiding the project and go on to explain the project design: its overarching objectives, the approach we are taking to implementation and evaluation, and the project activities themselves, including those that are emerging in response to physical distancing measures being taken in the context of the COVID-19 pandemic. Health and dementia in a Canadian context Any conversation about dementia finds itself inevitably in the context of health care. In Canada, our health care system is federally funded and provincially administered. Each province oversees its own health insurance plan, which is accountable under the Canada Health Act to provide medically necessary hospital and physician care services to all eligible Canadians.

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)0.012
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.185
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0290.025
Scholarly communication0.0180.011
Open science0.0050.019
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0200.001

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 head0.329
GPT teacher head0.431
Teacher spread0.102 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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