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Record W2769150390 · doi:10.12927/whp.2017.25309

The UN High-Level Commission on Health Employment and Economic Growth: The Opportunity for Communities and their Primary Health Systems

2017· article· en· W2769150390 on OpenAlexaffvenue
Judith Shamian, Kate Tulenko, Sandra MacDonald‐Rencz

Bibliographic record

VenueWorld health & population · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare Facilities Design and Sustainability
Canadian institutionsCARE Canada
Fundersnot available
KeywordsCommissionWork (physics)Health policyHealth careGlobal healthPublic healthEconomic growthBusinessPolitical scienceEnvironmental healthMedicineNursingEconomicsEngineeringLaw

Abstract

fetched live from OpenAlex

Focusing on the UN High-Level Commission on Health Employment and Economic Growth, this paper examines its potential impact on primary health-care to communities. It contains a set of curated interviews with key decision-makers who are determining how health workers are trained and employed all over the world. The commentaries come from individuals who have either been or have not been directly involved in the work of the Commission, exploring the necessary actions needed in support of implementing these recommendations, highlighting the ultimate potential impact at the local level - health systems and health workers working in communities and their primary health systems. Please note that the full submissions for these individuals are contained in Appendix 1 (available at: www.longwoods.com/content/25309).

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)0.021
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0050.005
Scholarly communication0.0110.004
Open science0.0020.006
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0100.001

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 head0.147
GPT teacher head0.389
Teacher spread0.242 · 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 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

Citations8
Published2017
Admission routes2
Has abstractyes

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