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Record W2092548316 · doi:10.1016/j.ijgo.2014.03.006

Changes in the use of manual vacuum aspiration for postabortion care within the public healthcare service network in Honduras

2014· article· en· W2092548316 on OpenAlexfundno aff
Ana Ligia Chinchilla, Ivo Flores Flores, A Morales, Marina Padilla de Gil

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2014
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsMedicineVacuum aspirationChristian ministryMedical emergencyHealth careAbortionService (business)Action planFamily planningNursingEnvironmental healthPopulationBusinessEconomic growthResearch methodologyPregnancy

Abstract

fetched live from OpenAlex

Honduras is one of the 17 priority countries included in the International Federation of Gynecology and Obstetrics (FIGO) Initiative for the Prevention of Unsafe Abortion and its Consequences. The priority category enables the country to request emergency funding to acquire services or commodities that could contribute toward achieving the objectives laid out in its plan of action. These objectives include improving postabortion care by increasing the use of manual vacuum aspiration (MVA) as an outpatient procedure with minimal human and material resources. Since the Ministry of Health lacked funding, use of the emergency fund was approved for the purchase and distribution of MVA kits nationwide to ensure continuity and the hope of increasing MVA use. Eleven hospitals participating in this initiative provided data for analysis of the outcome. These data show no increase in MVA use; however, as discussed in the article, further investigation provided valuable information on the reasons behind these results.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.174
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.364
Teacher spread0.278 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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