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Record W3003005031 · doi:10.1080/10705422.2020.1716911

Asset based community development to promote healthy aging in a rural context in Western Canada: notes from the field

2020· article· en· W3003005031 on OpenAlexaffabout
Karen Kobayashi, Denise Cloutier, Mushira Khan, Kyla Fitzgerald

Bibliographic record

VenueJournal of Community Practice · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsContext (archaeology)Exploratory researchPublic relationsAsset (computer security)Community developmentQualitative researchLocal authorityCommunity organizationPolitical scienceSociologyPublic administrationGeography

Abstract

fetched live from OpenAlex

Working from an asset-based perspective, this exploratory, qualitative study examined local community strengths and capacities to support healthy aging in a rural community in Western Canada. The research team consisted of academic leads and a project coordinator from a Canadian university, collaborating partners from the health authority, representatives from local and regional municipal leadership, and an Advisory Committee comprising local municipal leaders, seniors’ advocates, business owners, health care professionals and other community stakeholders. The findings from this study underscore five equally critical and overlapping areas of recommendations: Networking and Cross-Community Collaborations; Communications; Health and Social Care Initiatives; Transportation; and Housing.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.038
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.008
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.140
GPT teacher head0.470
Teacher spread0.330 · 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

Citations19
Published2020
Admission routes2
Has abstractyes

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