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Record W2128207193 · doi:10.1093/her/cyr029

A national survey of organizational transfer practices in chronic disease prevention in Canada

2011· article· en· W2128207193 on OpenAlexafffundabout
Nancy Hanusaik, Jennifer O’Loughlin, Gilles Paradis, Natalie Kishchuk

Bibliographic record

VenueHealth Education Research · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcGill UniversityCentre Hospitalier de l’Université de MontréalInstitut National de Santé Publique du QuébecMcGill University Health CentreUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsDisease preventionMedicineChronic diseaseFamily medicineEnvironmental healthGerontologyNursingPsychology

Abstract

fetched live from OpenAlex

Underuse of best practices in chronic disease prevention (CDP) represents missed opportunities to promote healthy living and prevent chronic disease. Better understanding of how CDP programs, practices and policies (PPPs) are transferred from 'resource' organizations that develop them to 'user' organizations that implement them is crucial. The objectives of this work were to develop psychometrically sound measures of transfer practices occurring within resource organizations; describe the use of these transfer practices and identify correlates of the transfer process. Cross-sectional data were collected in structured telephone interviews with the person most knowledgeable about PPP transfer in 77 Canadian organizations that develop PPPs. Independent correlates of transfer were identified using multiple linear regression. The transfer practices most commonly used included: identification of barriers to PPP adoption/implementation, tailoring transfer strategies and designing a transfer plan. Skill at planning/implementing transfer, external sources of funding specifically allocated for transfer, type of resource organization, attitude toward process of collaboration and user-centeredness were all positively associated with the transfer process. These factors represent possible targets for interventions to improve transfer of CDP PPPs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.471
GPT teacher head0.610
Teacher spread0.139 · 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 teacher head, not a consensus.

Study designObservational
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

Citations2
Published2011
Admission routes3
Has abstractyes

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