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Record W1890934114

여성건강연구 관련 법, 관리체계 및 연구현황에 대한 국가 비교

2014· article· ko· W1890934114 on OpenAlexaboutno aff
임도희, 이지혜, 김혜원, 박수현, 이혜아, 민정원, 박보현, 정최경희, 박혜숙

Bibliographic record

VenueJOURNAL OF THE KOREAN SOCIETY OF MATERNAL AND CHILD HEALTH · 2014
Typearticle
Languageko
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic growthHealth policyDeveloping countryInternational healthScale (ratio)Political scienceHRHISHealth promotionBusinessMedicineEnvironmental healthPublic healthNursingGeography
DOInot available

Abstract

fetched live from OpenAlex

Objectives : The purpose of this study was to provide evidence from the literature supporting the need for women’s health and wellbeing in South Korea. Method : We reviewed the information of national health system by collecting the women’s health related literature of USA, Canada and Australia to compare the South Korea health system to other advanced nations. Results : Compared with other advanced countries, women’s health infrastructure of South Korea seems to be insufficient, in terms of health access and research on women’s health. There are independent departments/agencies for women’s health in three advanced nations. These departments are responsible for developing national strategies, responding issues of women’s health needs, and implementing health policies. The governments of these countries support comparatively large scale of funding for projects and research of women’s health. Conclusions : Based on the findings, there is a need for strengthening infrastructure to improve women’s health in South Korea. We expect that the reviews presented in this study will serve as the base for developing policies and health plans for women’s health research in South Korea.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.024
GPT teacher head0.367
Teacher spread0.343 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJOURNAL OF THE KOREAN SOCIETY OF MATERNAL AND CHILD HEALTHSame topicHealth and Wellbeing ResearchFrench-language works237,207