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Record W2011861876 · doi:10.1177/1524839912461274

Making Health Promotion Evidenced-Informed

2012· article· en· W2011861876 on OpenAlexaff
Katie Dilworth, May Lin Tao, Sheree Shapiro, Carol Timmings

Bibliographic record

VenueHealth Promotion Practice · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsToronto Public Health
Fundersnot available
KeywordsExcellenceCandidacyHealth promotionPromotion (chess)Best practiceUnit (ring theory)NursingEvidence-based practiceMedical educationFocus groupMedicinePublic relationsPsychologyBusinessPublic healthAlternative medicinePolitical scienceMarketing

Abstract

fetched live from OpenAlex

This large urban health unit identified a need for explicit, strategic, long-term organizational priority toward practical application of evidence in health promotion practice. Becoming a Best Practice Spotlight Organization (BPSO®) candidate provided an opportunity to systematically implement this commitment. The primary goals were to support incorporation of evidence-informed practice throughout the organization, increase interprofessional collaboration, and provide opportunities for knowledge exchange for staff. A mixed-methods evaluation consisting of three phases, including an analysis of previous evaluations, a survey of Champions, and an online focus group with the Steering Committee, demonstrated very positive outcomes. Staff reported increased incorporation of evidence in practice and program delivery. Collaboration and consultation amongst interdisciplinary staff across program areas also increased and staff responded very positively to increased opportunities for knowledge exchange. BPSO® candidacy opportunities should be used by health organizations to increase evidence-informed practice and inspire excellence in health promotion practice.

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)0.040
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.067
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0100.005
Open science0.0020.014
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0100.002

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 head0.846
GPT teacher head0.753
Teacher spread0.093 · 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.

Study designTheoretical or conceptual
DomainMethods
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

Citations9
Published2012
Admission routes1
Has abstractyes

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