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Record W2003509631 · doi:10.1071/ah070422

Promoting evidence-based practice in population health at the local level: a case study in workforce capacity development

2007· article· en· W2003509631 on OpenAlexaff
Michelle Maxwell, Armita Adily, Jeanette Ward

Bibliographic record

VenueAustralian Health Review · 2007
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsCanadian Foundation for Healthcare Improvement
FundersAnhui University of Science and TechnologyNational Institute of Clinical StudiesAustralian National University
KeywordsPopulation healthWorkforce developmentWorkforceCapacity buildingHealth economicsProject commissioningWorkforce planningPopulationService (business)Training and developmentHealth careEvidence-based practiceNursingPublic healthHuman resourcesPublic relationsMedicineBusinessPublishingManagementEconomic growthMarketingEnvironmental healthPolitical scienceAlternative medicineEconomics

Abstract

fetched live from OpenAlex

This paper describes a service-based initiative to enhance capacity for evidence-based practice (EBP) in the South Western Sydney Area Health Service Division of Population Health. A working group planned an organisational response to a customised EBP needs assessment using the New South Wales Department of Health's framework for capacity building focussing on five key action areas; organisational development, workforce development, resource allocation, leadership and partnerships. Innovative strategies to promote EBP were developed and implemented and on-site training programs that targeted specific groups of staff were conducted. Because there was commitment and leadership from senior staff for the initiative, a comprehensive approach to building capacity for EBP in population health was possible. Evidence of impact needs to be collected in the future.

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)0.039
metaresearch head score (Gemma)0.041
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 score0.039
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.041
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0110.005
Scholarly communication0.0040.004
Open science0.0030.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.425
GPT teacher head0.554
Teacher spread0.129 · 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

Citations13
Published2007
Admission routes1
Has abstractyes

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