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Record W7095369554

Psychological distress one year after childbirth

2014· article· en· W7095369554 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Comparative Analysis Research
Canadian institutionsnot available
Fundersnot available
KeywordsDistressGeneral Health QuestionnairePsychological distressMultivariate analysisChildbirthBivariate analysisMental health
DOInot available

Abstract

fetched live from OpenAlex

Background: The aim of this study was to compare rates of psychological distress, one year after childbirth, between women living in France, Italy and Quebec (Canada). Methods: Analysis was performed for a sample of 1663 mothers of one-year-old children. Psychological distress rates, measured by the General Health Questionnaire and a measure of psychotropic drug use, were compared in bivariate and multivariate analysis. Results: According to the GHQ scores, the psychological distress rate was significantly higher in Quebec (16 % scoring over 5) than in France (11%) and Italy (9%). Differences between countries were most marked for women with a low level of education, who were more distressed in Quebec than elsewhere. However, psychotropic drug use was significantly more common in France than in Italy and Quebec. When the two indicators of psychological distress were combined (high GHQ score or use of psychotropic drugs), France and Quebec presented similar distress rates, both significantly different from Italy. Conclusion: Differences in the expression of distress may partly account for the observed differences in distress rate between countries. However, the social structure of each country probably plays a role in the development of psychological distress, possibly leading to differences in distress rate between countries.

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)0.004
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 score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
GPT teacher head0.488
Teacher spread0.367 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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