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Record W3156385191 · doi:10.2196/25407

Protocol for the Pregnancy During the COVID-19 Pandemic (PdP) Study: A Longitudinal Cohort Study of Mental Health Among Pregnant Canadians During the COVID-19 Pandemic and Developmental Outcomes in Their Children

2021· article· en· W3156385191 on OpenAlexafffundvenueabout
Gerald F. Giesbrecht, Mercedes Bagshawe, Melinda van Sloten, Anna MacKinnon, Ashley Dhillon, Marcel van de Wouw, Elnaz Vaghef‐Mehrabany, Laura Rojas, Danielle Cattani, Catherine Lebel, Lianne Tomfohr‐Madsen

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaAlberta Children's Hospital Research InstituteCanadian Child Health Clinician Scientist ProgramUniversity of Calgary
KeywordsMedicinePsychosocialPregnancyAnxietyLongitudinal studyPandemicMental healthPublic healthCohort studySocioeconomic statusCohortDistressPopulationDemographyPsychiatryClinical psychologyCoronavirus disease 2019 (COVID-19)Environmental healthDiseaseNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic and countermeasures implemented by governments around the world have led to dramatically increased symptoms of depression and anxiety. Pregnant individuals may be particularly vulnerable to the negative psychological effects of COVID-19 public health measures because they represent a demographic that is most affected by disasters and because pregnancy itself entails significant life changes that require major psychosocial and emotional adjustments. OBJECTIVE: The PdP study was designed to investigate the associations among exposure to objective hardship caused by the pandemic, perceived stress and psychological distress in pregnant individuals, and developmental outcomes in their offspring. METHODS: The PdP study comprises a prospective longitudinal cohort of individuals who were pregnant at enrollment, with repeated follow-ups during pregnancy and the postpartum period. Participants were eligible if they were pregnant, ≥17 years old, at ≤35 weeks of gestation at study enrollment, living in Canada, and able to read and write in English or French. At enrollment, participants completed an initial survey that assessed demographic and socioeconomic characteristics, previous pregnancies and births, prepregnancy health, health conditions during pregnancy, medications, psychological distress, social support, and hardships experienced because of the COVID-19 pandemic (eg, lost employment or a loved one dying). For the first three months following the initial survey, participants received a monthly email link to complete a follow-up survey that asked about their experiences since the previous survey. After three months, follow-up surveys were sent every other month to reduce participant burden. For each of these surveys, participants were first asked if they were still pregnant and then routed either to the next prenatal survey or to the delivery survey. In the postpartum period, surveys were sent at 3, 6, and 12 months of infant age to assess maternal stress, psychological distress, and infant development. RESULTS: Participant recruitment via social media (Facebook and Instagram) began on April 5, 2020, and is ongoing. As of April 2021, more than 11,000 individuals have started the initial survey. Follow-up data collection is ongoing. CONCLUSIONS: This longitudinal investigation seeks to elucidate the associations among hardships, maternal psychological distress, child development during the COVID-19 pandemic, and risk and resilience factors that amplify or ameliorate these associations. The findings of this study are intended to generate knowledge about the psychological consequences of pandemics on pregnant individuals and point toward prevention and intervention targets. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25407.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.079
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.240
GPT teacher head0.521
Teacher spread0.282 · 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.

Study designObservational
Domainnot available
GenreProtocol

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

Citations46
Published2021
Admission routes4
Has abstractyes

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