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Record W2010427756 · doi:10.1186/1478-4491-7-67

Equity-oriented toolkit for health technology assessment and knowledge translation: application to scaling up of training and education for health workers

2009· article· en· W2010427756 on OpenAlexafffund
Erin Ueffing, Peter Tugwell, Janet Hatcher Roberts, Peter G. Walker, Nadia Hamel, Vivian Welch

Bibliographic record

VenueHuman Resources for Health · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCanadian Society for International HealthInstitute of Population and Public HealthUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsWorkforceEquity (law)Knowledge translationHealth equityAllianceGlobal healthHealth services researchHealth policyHealth administrationHealth promotionBusinessInternational healthPublic relationsMedicinePublic healthKnowledge managementEconomic growthNursingPolitical scienceEconomicsComputer science

Abstract

fetched live from OpenAlex

Human resources for health are in crisis worldwide, especially in economically disadvantaged areas and areas with high rates of HIV/AIDS in both health workers and patients. International organizations such as the Global Health Workforce Alliance have been established to address this crisis. A technical working group within the Global Health Workforce Alliance developed recommendations for scaling up education and training of health workers. The paper will illustrate how decision-makers can use evidence and tools from an equity-oriented toolkit to scale up training and education of health workers, following five recommendations of the technical working group. The Equity-Oriented Toolkit, developed by the World Health Organization Collaborating Centre for Knowledge Translation and Health Technology Assessment in Health Equity, has four major steps: (1) burden of illness; (2) community effectiveness; (3) economic evaluation; and (4) knowledge translation/implementation. Relevant tools from each of these steps will be matched with the appropriate recommendation from the technical working group.

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)0.014
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.436
GPT teacher head0.539
Teacher spread0.103 · 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 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

Citations16
Published2009
Admission routes2
Has abstractyes

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