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Record W4206289802 · doi:10.21203/rs.3.rs-1167874/v1

The COVID-19 pandemic impacted maternal mental health differently depending on pregnancy status and trimester of gestation

2022· preprint· en· W4206289802 on OpenAlexaff
Anick Bérard, Jessica Gorgui, Vanina Tchuente, Anaïs Lacasse, Yessica-Haydee Gomez, Sylvana M. Côté, Suzanne King, Flory T. Muanda, Yves Mufike, Isabelle Boucoiran, Anne Monique Nuyt, Caroline Quach, Ema Ferreira, Padma Kaul, Brandace Winquist, Kiernan O'Donnell, Sherif Eltonsy, Dan Château, Jin‐Ping Zhao, Gillian E. Hanley, Tim F. Oberlander, Behrouz Kassaï, Sabine Mainbourg, Sasha Bernatsky, Évelyne Vinet, Annie Brodeur-doucet, Jackie Demers, Philippe Richebé, Valérie Zaphiratos

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of British ColumbiaManitoba HealthUniversity of ManitobaMcGill UniversityUniversité du Québec à Trois-RivièresHôpital Maisonneuve-RosemontWestern UniversityCentre Hospitalier Universitaire Sainte-JustineUniversity of SaskatchewanUniversity of AlbertaUniversité de Montréal
Fundersnot available
KeywordsPregnancyPandemicCoronavirus disease 2019 (COVID-19)GestationObstetricsMedicineFirst trimester2019-20 coronavirus outbreakSecond trimesterMaternal healthSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Environmental healthVirologyHealth servicesBiologyPopulationInfectious disease (medical specialty)Internal medicineDisease

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.008
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 score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.138
GPT teacher head0.476
Teacher spread0.338 · 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

Citations5
Published2022
Admission routes1
Has abstractno

Explore more

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