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Record W2785441644 · doi:10.12927/whp.2017.25310

Facing a Paradigm Shift in the Sustainable Development Goal Era

2017· article· en· W2785441644 on OpenAlexaffvenue
Allison Annette Foster, Gail Tomblin Murphy, Vic Neufeld

Bibliographic record

VenueWorld health & population · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of VictoriaDalhousie University
Fundersnot available
KeywordsSustainable developmentParadigm shiftGlobal healthEconomic growthPolitical scienceHealth careEconomics

Abstract

fetched live from OpenAlex

The Sustainable Development Goals challenge us to step beyond traditional development approaches and to consider strategies that are evidence informed and innovative. The concepts are familiar; themes aligned with Harmonization, Primary Healthcare, Leadership, Public Private Partnerships, Community Engagement, and Integrated Technologies. However, to optimize resources and overcome today's challenge with sustainable solutions, we must capture lessons learned and apply evidence developed to inform and expand the thinking to shape and inform new paradigms. The tools, the experience, and the evidence are at our finger-tips. We must hold ourselves accountable to turn that rudder and hold the line so that the ship can advance toward universal health coverage that ensures healthy lives and promotes wellbeing for all at all ages. Health is where economic well-being, labour opportunities, educational advancement, gender equity and access to food, water, clean air come together to advance the wellbeing of all. This juncture is most significant at community level, where health systems intertwine with the social and cultural fabric and health workers stand at the interface between the health system and the people it serves. In these manuscripts, thought leaders in the health sector share evidence and experience to help us consider how we will use this intersection to push all nations to achieve all the SDGs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.031
GPT teacher head0.352
Teacher spread0.321 · 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 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

Citations6
Published2017
Admission routes2
Has abstractyes

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