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Record W2597098301 · doi:10.1177/084456211404600108

Promoting Health Equity Research: Insights from a Canadian Initiative

2014· article· en· W2597098301 on OpenAlexaffvenueabout
Miriam J. Stewart, Kaysi Eastlick Kushner

Bibliographic record

VenueCanadian Journal of Nursing Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEquity (law)Political sciencePsychological interventionLibrary sciencePublic relationsMedicineNursing

Abstract

fetched live from OpenAlex

In 2002 the Canadian Institutes of Health Research launched a national initiative to promote health equity research reflecting the World Health Organization imperative of investment in health equity research. Funded researchers and teams have investigated health disparities faced by vulnerable populations, analyzed interactions of health determinants, and tested innovative interventions. Strategies for building research capacity have supported students, postdoctoral fellows, new investigators, and interdisciplinary research teams. Partnerships have been created with 10 national and 7 international organizations. Strategies used to secure and sustain this research initiative could be adapted to other contexts. Nurse scholars led the launch and have sustained the legacy of this national research initiative. Moreover, nurse researchers and research trainees, supported by the initiative, have contributed to the expansion and translation of the health equity knowledge base.

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)0.075
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.864

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0550.026
Scholarly communication0.0260.008
Open science0.0050.023
Research integrity0.0080.016
Insufficient payload (model declined to judge)0.0040.000

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 head0.473
GPT teacher head0.558
Teacher spread0.085 · 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.

Study designQualitative
DomainMethods
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

Citations1
Published2014
Admission routes3
Has abstractyes

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