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Record W6920643870 · doi:10.60692/nfkcc-m4011

How do we reach the girls and women who are the hardest to reach? Inequitable opportunities in reproductive and maternal health care services in armed conflict and forced displacement settings in Colombia

2018· article· en· W6920643870 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsCanadian Council on International LawUniversity of Ottawa
Fundersnot available
KeywordsInequalityReproductive healthHealth careHealth equitySocial inequalityHealth policyPublic health

Abstract

fetched live from OpenAlex

This paper assesses inequalities in access to reproductive and maternal health services among females affected by forced displacement and sexual and gender-based violence in conflict settings in Colombia. This was accomplished through the following approaches: first, we assessed the gaps and gradients in three selected reproductive and maternal health care services. Second, we analyzed the patterns of inequalities in reproductive and maternal health care services and changes over time. And finally, we identified challenges and strategies for reaching girls and women who are the hardest to reach in conflict settings, in order to accelerate progress towards universal health coverage and to contribute to meeting the Sustainable Development Goals of good health and well-being and gender equality by 2030.Three types of data were required: data about health outcomes (relating to rates of females affected by conflict), information about reproductive and maternal health care services to provide a social dimension to unmask inequalities (unmet needs in family planning, antenatal care and skilled births attendance); and data on the female population. Data sources used include the National Information System for Social Protection, the National Registry of Victims, the National Administrative Department of Statistics, and Demographic Health Survey at three specific time points: 2005, 2010 and 2015. We estimated the slope index of inequality to express absolute inequality (gaps) and the concentration index to expresses relative inequality (gradients), and to understand whether inequality was eliminated over time.Our findings show that even though absolute health care service-related inequalities dropped over time, relative inequalities worsened or remain unchanged. All summary measures still indicated the existence of inequalities as well as common patterns. Our findings suggest that there is a pattern of marginal exclusion and incremental patterns of inequality in the reproductive and maternal health care service provided to female affected by armed conflict.Overall, the effects of conflict continue to threaten reproductive and maternal health in Colombia, impeding progress towards the realization of universal health care (UHC) and reinforcing already-existing inequities. Key messages and steps forward include the need to understand the two distinct patterns of inequalities identified in this study in order to prompt improved general policy responses. Addressing unmet needs in reproductive and maternal health requires supporting gender equality and prioritizing the girls and women in regions with the highest rates of victims of armed conflict, with the objective of leaving no girl or woman behind. This analysis represents the first attempt to analyze coverage-related inequality in reproductive and maternal health care services for female affected by armed conflict in Colombia. As the World Health Organization and global health systems leaders call for more inclusive engagement, this approach may serve as the key to shaping people-centred health systems. In this particular case, health care facilities must be located in close proximity to girls and women in conflict and post-conflict settings in order to deliver essential reproductive and maternal health care services. Finally, reducing inequalities in opportunities would not only promote equity, but also drive sustainable development.

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)0.001
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 score0.041
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.031
GPT teacher head0.250
Teacher spread0.220 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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