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Record W2228387232 · doi:10.5539/gjhs.v8n9p121

The Relationship between Intimate Partner Violence and Family Planning among Girls and Young Women in the Philippines

2016· article· en· W2228387232 on OpenAlexvenueno aff
Laura Cordisco Tsai, Claudia Cappa, Nicole Petrowski

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsDomestic violenceFamily planningReproductive healthSexual violenceMedicineOddsLogistic regressionPoison controlPsychologyDemographySuicide preventionPopulationEnvironmental healthNursingSociology

Abstract

fetched live from OpenAlex

This study explored the relationship between intimate partner violence (IPV) and family planning among adolescent girls and young women in formal unions in the Philippines. Analyzing a sample (n =1,566) from the 2013 Philippines Demographic and Health Survey, logistic regression models were separately run for current contraception use and unmet need for family planning on recent physical violence (yes/no), recent sexual violence (yes/no), and recent emotional (yes/no). Findings revealed that the odds of using contraception were significantly higher among girls and young women who reported recent physical IPV (OR=1.84; 95% CI=1.13, 2.99; p<0.05) and sexual IPV (OR=2.18; 95% CI=1.17, 4.06; p<0.05). No significant relationship between recent emotional IPV and contraception use was found. Having an unmet need for family planning showed no significant relationship to IPV. The study adds to a growing body of literature revealing a positive association between IPV and contraception use. Findings hold implications for the provision of family planning services for adolescents and young women in response to the recent passage of landmark legislation pertaining to reproductive health in the Philippines, the Responsible Parenthood and Reproductive Health Act.

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)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.059
GPT teacher head0.390
Teacher spread0.331 · 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.

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

Citations20
Published2016
Admission routes1
Has abstractyes

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