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Do family-planning workers in China support provision of sexual and reproductive health services to unmarried young people?

2004· article· en· W2100075591 on OpenAlexaboutno aff
Xiaowen Tu, Nian Cui, Chaohua Lou, Ersheng Gao

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsFamily planningReproductive healthMedicineChinaGovernment (linguistics)Focus groupQuarter (Canadian coin)PopulationEnvironmental healthBusinessPolitical scienceGeography

Abstract

fetched live from OpenAlex

OBJECTIVE: To ascertain the perspectives of family-planning service providers in eight sites in China on the provision of sexual and reproductive health services to unmarried young people. METHODS: Data were drawn from a survey of 1927 family-planning workers and 16 focus group discussions conducted in eight sites in China in 1998-99. FINDINGS: Family-planning workers recognized the need to protect the sexual health of unmarried young people and were unambiguous about the need for government agencies to provide information and education on sexual and reproductive health to unmarried young people; however, perceptions about the appropriate age for and content of such education remained conservative. While about 70% of family-planning workers were willing to provide contraceptives to unmarried young people, and about 60% approved government provision of contraceptive services to unmarried young people, only one quarter agreed that the services could be extended to senior high schools. CONCLUSION: Family-planning workers in China are ambivalent about the provision of sexual and reproductive health services to unmarried young people, which potentially poses a significant obstacle to the adoption of safe sex behaviours by young people, as well as to the provision of sexual and reproductive health information and services to young unmarried people in China. Training programmes for family-planning workers are urgently needed to address this issue.

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 score0.198
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.301
Teacher spread0.272 · 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

Citations35
Published2004
Admission routes1
Has abstractyes

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