MétaCan
Menu
Back to cohort
Record W4386640633 · doi:10.3389/phrs.2023.1606110

The Governance of Core Competencies for Public Health: A Rapid Review of the Literature

2023· review· en· W4386640633 on OpenAlexafffund
Harman Singh Sandhu, Victoria Otterman, Lynda Tjaden, Rosemarie Shephard, Emma Apatu, Erica Di Ruggiero, Richard Musto, Jasmine Pawa, Malcolm Steinberg, Claire Betker

Bibliographic record

VenuePublic health reviews · 2023
Typereview
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsMcMaster UniversityCanadian Public Health AssociationImpactPublic Health Agency of CanadaNOSM UniversityPublic Health OntarioUniversity of TorontoSimon Fraser UniversitySt. Francis Xavier UniversityHealth Canada
FundersPublic Health AgencyPublic Health Agency of CanadaMcMaster University
KeywordsDelphi methodPublic healthCorporate governanceCore competencyWorkforceDelphiMedicinePolitical scienceKnowledge managementPublic relationsBusinessPsychologyNursingComputer science

Abstract

fetched live from OpenAlex

Core competencies for public health (CCPH) define the knowledge, skills, and attitudes required of a public health workforce. Although numerous sets of CCPH have been established, few studies have systematically examined the governance of competency development, review, and monitoring, which is critical to their implementation and impact. This rapid review included 42 articles. The findings identified examples of collaboration and community engagement in governing activities (e.g., using the Delphi method to develop CCPH) and different ways of approaching CCPH review and revision (e.g., every 3 years). Insights on monitoring and resource management were scarce. Preliminary lessons emerging from the findings point towards the need for systems, structures, and processes that support ongoing reviews, revisions, and monitoring of CCPH.

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)0.020
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0190.015
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.568
GPT teacher head0.563
Teacher spread0.005 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuePublic health reviewsSame topicPublic Health Policies and EducationFrench-language works237,207