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Record W2105287696 · doi:10.1080/07399332.2011.603872

The Health of Women and Girls Determines the Health and Well-Being of Our Modern World: A White Paper From the International Council on Women's Health Issues

2011· article· en· W2105287696 on OpenAlexaff
Patricia M. Davidson, Sarah J. McGrath, Afaf Ibrahim Meleis, Phyllis Noerager Stern, Michelle DiGiacomo, Tessa Dharmendra, Rosaly Correa‐de‐Araujo, Jacquelyn C. Campbell, Margarethe Hochleitner, DeAnne K. Hilfinger Messias, Hazel N. Brown, Anne M. Teitelman, Siriorn Sindhu, Karen Reesman, Solina Richter, Marilyn S. Sommers, Doris Schaeffer, Marilyn Stringer, Carolyn M. Sampselle, Debra Anderson, Josefina A. Tuazon, Yingjuan Cao, Eleanor Krassen Covan

Bibliographic record

VenueHealth Care For Women International · 2011
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Alberta
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsEmpowermentGlobeGlobal healthGirlMultidisciplinary approachHealth careInternational healthHealth educationPolitical sciencePublic relationsNursingEconomic growthPsychologyMedicineLaw

Abstract

fetched live from OpenAlex

The International Council on Women's Health Issues (ICOWHI) is an international nonprofit association dedicated to the goal of promoting health, health care, and well-being of women and girls throughout the world through participation, empowerment, advocacy, education, and research. We are a multidisciplinary network of women's health providers, planners, and advocates from all over the globe. We constitute an international professional and lay network of those committed to improving women and girl's health and quality of life. This document provides a description of our organization mission, vision, and commitment to improving the health and well-being of women and girls globally.

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)0.010
metaresearch head score (Gemma)0.006
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: Other · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.005
Scholarly communication0.0090.005
Open science0.0010.003
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.307
Teacher spread0.279 · 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
GenreOther

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

Citations89
Published2011
Admission routes1
Has abstractyes

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