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Record W4364323308 · doi:10.35493/medu.43.22

Perinatal Mental Health in Hamilton and Montreal

2023· article· en· W4364323308 on OpenAlexaffvenueabout
Shanzey Ali, Suraj Bansal, Ines Durant, Sofia Reynoso, Tiffany Spector, Jeffrey Sun, Yiming Zhang

Bibliographic record

VenueThe Meducator · 2023
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsMcGill UniversityMcMaster University
Fundersnot available
KeywordsMental healthMandatePsychiatryMedicineMental illnessChildbirthPublic healthPregnancyPsychologyNursingPolitical science

Abstract

fetched live from OpenAlex

Mental health concerns experienced by individuals during or up to one year after pregnancy fall under the umbrella of perinatal mental health. Estimates suggest that one in five people will experience a perinatal mental illness at some point during their pregnancy or up to one year postpartum. Racialized individuals with low socioeconomic status are at an increased risk to perinatal mental health. Perinatal mental illness is becoming an incredibly relevant topic in the sphere of public health and policy. Recently, the Canadian Task Force on Preventive Health Care released a recommendation against screening individuals for depression during pregnancy and the postpartum period (up to 1 year after childbirth), stating that there is low certainty of evidence for such screening measures. This received backlash, with groups like the Canadian Perinatal Mental Health Collaborative (CPMHC) speaking out. Prime Minister Justin Trudeau and the Minister of Mental Health and Addictions aim to “ensure timely access to perinatal mental health services”, as identified in a recent mandate letter. Given its current salience, this piece seeks to explore perinatal mental health programs in Ontario and Quebec, critically analyze their effectiveness, and suggest future areas of improvement.

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)0.000
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 score0.434
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.323
Teacher spread0.304 · 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
Published2023
Admission routes3
Has abstractyes

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