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Record W1987168454 · doi:10.1080/08959420.2014.854606

Lessons Learned From a Canadian Province-Wide Age-Friendly Initiative: The Age-Friendly Manitoba Initiative

2013· article· en· W1987168454 on OpenAlexaffabout
Verena Menec, Sheila Novek, DAWN M. VESELYUK, Jennifer McArthur

Bibliographic record

VenueJournal of Aging & Social Policy · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGovernment (linguistics)Promotion (chess)Formative assessmentEnvironmentally friendlyPolitical scienceUser FriendlyEconomic growthPsychology

Abstract

fetched live from OpenAlex

The Age-Friendly Manitoba Initiative was launched in 2008. A formative evaluation we conducted in 2011 with 44 participating rural and urban communities demonstrates considerable progress, with virtually all communities having formed an Age-Friendly Committee and conducting a community assessment to identify priorities for action. The majority of communities implemented one or more age-friendly projects. Major barriers to becoming age-friendly identified by participants included lack of funding; lack of capacity, particularly in small communities; and lack of leadership or direction. The study highlights the importance of strong leadership at all levels of government (municipal, provincial, federal); the need to support communities, particularly rural ones, as they try to become more age-friendly; and the importance of ongoing promotion of age-friendliness locally and more broadly (e.g., provincially).

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)0.031
metaresearch head score (Gemma)0.026
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: none
Teacher disagreement score0.077
Threshold uncertainty score0.561

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0210.007
Scholarly communication0.0090.003
Open science0.0050.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.120
GPT teacher head0.441
Teacher spread0.321 · 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

Citations58
Published2013
Admission routes2
Has abstractyes

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