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Record W2989556408 · doi:10.1177/0733464819886449

Engaging in Community Dialogues on Low-Risk Alcohol Use Guidelines for Older Adults

2019· article· en· W2989556408 on OpenAlexaff
Sarah L. Canham, Joe Humphries, Anthony L. Kupferschmidt, Emily Lonsdale

Bibliographic record

VenueJournal of Applied Gerontology · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHealth promotionPsychologyQualitative researchAlcohol consumptionPromotion (chess)GerontologyMedicineMedical educationNursingPublic healthAlcoholSociologyPolitical science

Abstract

fetched live from OpenAlex

Despite widespread use and acceptance of alcohol, discussions of age-related changes that impact alcohol consumption behaviors are rare. The objective of this community-engaged qualitative research study was to gain insight into how to promote knowledge dissemination regarding newly developed low-risk drinking guidelines for older adults. A convenience sample of 66 older adults and service providers participated in three Knowledge Café dialogue workshops and discussed their opinions about alcohol use in later life and ideas for sharing alcohol-related research evidence with the community. Participants discussed (a) low-risk drinking knowledge dissemination, (b) personal choice in drinking alcohol and adherence to low-risk drinking guidelines, and (c) preferences for engaging in discussions about alcohol use. Community dialogues fostered knowledge dissemination while participants engaged in rich conversations about a rarely discussed topic. Sharing evidence-based clinical advice with community stakeholders through dialogue events offers an innovative opportunity for health promotion efforts.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.634
GPT teacher head0.626
Teacher spread0.008 · 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.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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