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Record W2110733691 · doi:10.5402/2011/456012

Postdischarge Impact of C-L Psychiatry Treatment in Obstetrical Inpatients

2011· article· en· W2110733691 on OpenAlexaff
Eileen P. Sloan, Sharon Kirsh

Bibliographic record

VenueISRN Obstetrics and Gynecology · 2011
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineFeelingMoodReferralPsychiatryMental healthFamily medicinePsychology

Abstract

fetched live from OpenAlex

Purpose. Twenty-eight women, referred to C-L Psychiatry during their obstetrical inpatient stay were interviewed six months post-discharge to determine how they experienced the consultation process, whether they recollected and adhered to treatment recommendations, and whether they developed or had a recurrence of mental health problems post-discharge. Method. Semi-structured telephone interviews were conducted by a psychologist who had not been involved with patient care. Results. There was strong congruence between reason for referral as stated in psychiatric consult notes and participants' recollections and strong congruence and compliance regarding treatment recommendations. Sixty-four percent of women had concerns regarding mood post-discharge, of whom 66% sought professional help within six months. Participants' recommendations for improving the effectiveness of the C-L service to obstetrical inpatients pertained mainly to sensitivity to patients' feelings, consistency of message and personnel, and post-discharge follow-up. Conclusions. Obstetrical patients had good recollection of their experience of C-L psychiatry, and post-discharge compliance with treatment recommendations was high. A post-discharge telephone call might further enhance treatment compliance and encourage women who are struggling with mood difficulties to seek help. Contact between C-L psychiatry and patients' primary care physician may also enhance care post-discharge.

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 score0.008
Threshold uncertainty score0.421

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 head0.035
GPT teacher head0.313
Teacher spread0.278 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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Same venueISRN Obstetrics and GynecologySame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207