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Record W2786549615 · doi:10.12927/whp.2017.25304

Transforming Health Workers’ Education for Universal Health Coverage: Global Challenges and Recommendations

2017· article· en· W2786549615 on OpenAlexvenueno aff
Timothy Evans, Edson Araújo, Christopher H. Herbst, Ok Pannenborg

Bibliographic record

VenueWorld health & population · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceParadigm shiftHealth policyEconomic growthContext (archaeology)Health educationGlobal healthInternational healthBusinessPublic relationsMedicinePolitical scienceHealth careEconomics

Abstract

fetched live from OpenAlex

Health workforce challenges remain a critical bottleneck in achieving universal health coverage (UHC) goals in most countries. As it stands, health professional training is primarily clinical, curricular and delinked from the needs of the health system. To achieve global health goals and maximize opportunities for employment and economic growth, all in the context of limited fiscal realities, a paradigm shift is needed with respect to the health workforce and corresponding education systems. There is a need to shift towards fair, gender friendly employment at a rate that matches the overall growth of the health economy, which acknowledges the role of the private sector in education and training. This paper emphasizes the importance and implications of such a paradigm shift. It argues the need for a 21st century framework for health professional education. This framework should represent a more satisfactory interface between supply and demand for health professional labor, in line with the need for UHC, job creation and economic growth.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.090
GPT teacher head0.359
Teacher spread0.269 · 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.

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

Citations5
Published2017
Admission routes1
Has abstractyes

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