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Record W3032955587 · doi:10.25561/71269

WHO competency framework for health workers’ education and training on antimicrobial resistance

2018· article· en· W3032955587 on OpenAlexfundno aff

Bibliographic record

VenueSpiral (Imperial College London) · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsnot available
FundersCenters for Disease Control and PreventionUniversité de GenèveUniversité de LorraineFudan UniversityUniversity of Cape TownPublic Health EnglandMonash UniversityUniversity of DundeeImperial College LondonTribhuvan UniversityInyuvesi Yakwazulu-NataliUniversity of OttawaPublic Health Agency of CanadaPublic Health Agency
KeywordsTraining (meteorology)Medical educationAntibiotic resistanceMedicineGeography

Abstract

fetched live from OpenAlex

In support of WHO and Member States efforts to implement the Global Action plan on Antimicrobial Resistance (GAP AMR), WHO has published a competency framework for health workers’ education and training on antimicrobial resistance (AMR). The competency framework is one of several products being developed by WHO in collaboration with partners and leading research institutions to address the first objective of the GAP AMR, which is to improve awareness and understanding of AMR through effective communication, education and training. The framework is a matrix menu of core and additional knowledge, skills and attitudes for health workers in the field of human health. It is designed to be used as a reference guide and applied according to local priorities and needs. The ultimate aim is to ensure that all health workers are equipped with the requisite competencies at pre-service education and in-service training levels to address AMR in policy and practice settings.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.285
Teacher spread0.268 · 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 designNot applicable
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

Citations27
Published2018
Admission routes1
Has abstractyes

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