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Record W3045630316 · doi:10.3390/ijerph17155421

Interactions between the Physical and Social Environments with Adverse Pregnancy Events Related to Placental Disorders—A Scoping Review

2020· article· en· W3045630316 on OpenAlexaff
Yolisa Prudence Dube, Newton Nyapwere, Laura A. Magee, Marianne Vidler, Sophie E. Moore, Benjamin Barratt, Rachel Craik, Peter von Dadelszen, Prestige Tatenda Makanga

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersMedical Research CouncilNational Institute for Health and Care ResearchKing's College LondonUK Research and Innovation
KeywordsAttendancePsychological interventionSocial determinants of healthDiseaseEnvironmental healthSocial supportPsychologyMedicinePublic healthSocial psychologyPsychiatryPolitical scienceNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Due to different social and physical environments across Africa, understanding how these environments differ in interacting with placental disorders will play an important role in developing effective interventions. METHODS: A scoping review was conducted, to identify current knowledge on interactions between the physical and social environment and the incidence of placental disease in Africa. RESULTS: Heavy metals were said to be harmful when environmental concentrations are beyond critical limits. Education level, maternal age, attendance of antenatal care and parity were the most investigated social determinants. CONCLUSIONS: More evidence is needed to determine the relationships between the environment and placental function in Africa. The results show that understanding the nature of the relationship between social determinants of health (SDH) and placental health outcomes plays a pivotal role in understanding the risk in the heterogenous communities in Africa.

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)0.006
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.409
Teacher spread0.352 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

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