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Record W2130910886 · doi:10.1139/jpn.0534

Sleep and perinatal mood disorders: a critical review

2005· review· en· W2130910886 on OpenAlexaffvenue
Lori E. Ross, Brian J. Murray, Meir Steiner

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2005
Typereview
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonHealth Sciences CentreSunnybrook Health Science CentreCentre for Addiction and Mental Health
Fundersnot available
KeywordsActigraphyMood disordersPostpartum depressionMoodPsychiatryPolysomnographySleep (system call)Postpartum psychosisPostpartum periodPsychologyDepression (economics)Sleep deprivationMedicinePerinatal periodPsychosisPregnancyClinical psychologyBipolar disorderInsomniaAnxietyElectroencephalographyCognition

Abstract

fetched live from OpenAlex

Pregnancy and the postpartum period are recognized as times of vulnerability to mood disorders, including postpartum depression and psychosis. Recently, changes in sleep physiology and sleep deprivation have been proposed as having roles in perinatal psychiatric disorders. In this article we review what is known about changes in sleep physiology and behaviour during the perinatal period, with a focus on the relations between sleep and postpartum "blues," depression and psychosis and on sleep-based interventions for the treatment and prevention of perinatal mood disorders. The interaction between sleep and perinatal mood disorders is significant, but evidence-based research in this field is limited. Studies that measure both sleep and mood during the perinatal period, particularly those that employ objective measurement tools such as polysomnography and actigraphy, will provide important information about the causes, prevention and treatment of perinatal mood disorders.

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)0.002
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.033
GPT teacher head0.370
Teacher spread0.337 · 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

Citations209
Published2005
Admission routes2
Has abstractyes

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