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Record W2996104693 · doi:10.17157/mat.6.4.732

Engaging Afghan men at a societal level to increase women’s access to contraception

2019· article· en· W2996104693 on OpenAlexfundno aff
Andrew Macnab, Wais Aria, Josephine de Freitas

Bibliographic record

VenueMedicine Anthropology Theory · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsAfghanMedicineReproductive healthChild marriageIntervention (counseling)PopulationIslamDemographyPsychologyPolitical scienceNursingEnvironmental healthSociologyGeographyLaw

Abstract

fetched live from OpenAlex

Mother and child mortality rates in Afghan internally displaced person (IDP) camps are high. Most women have unplanned pregnancies; many are child brides. Contraception can lower maternal mortality/morbidity from unintended pregnancy and short birth intervals, but in Afghan culture men make health decisions. Using knowledge of Afghan society gleaned from Afghan health workers, we trained respected elders and imams to start men’s groups to share practical, financial, and religious facts about contraception and promote discussion. The aim was to inform and allay misconceptions and fears so informed spousal conversation could occur; the overall objective was to allow women wanting spaced pregnancies or smaller families to gain spousal understanding and approval for use of contraception. Societal responses were monitored for one year among nine hundred families in three IDP camps where weekly men’s groups were conducted. Taking photographs captured unique ethnographic aspects of the intervention especially for those involved in this research who were not in Afghanistan and hence missed being directly engaged with the community. The maternal and infant health challenges and cultural issues addressed are global in nature; this model is globally applicable to other camps and Islamic societies.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0470.004

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 head0.105
GPT teacher head0.493
Teacher spread0.387 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations4
Published2019
Admission routes1
Has abstractyes

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