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Record W4291618518 · doi:10.1177/10497323221120173

Seeing Red: A Grounded Theory Study of Women’s Anger after Childbirth

2022· article· en· W4291618518 on OpenAlexafffund
Christine Ou, Wendy A. Hall, Paddy Rodney, Robyn Stremler

Bibliographic record

VenueQualitative Health Research · 2022
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaUniversity of Victoria
FundersSchool of Nursing, University of British ColumbiaUniversity of British Columbia Graduate School
KeywordsAngerResentmentChildbirthGrounded theoryDistressPsychologyDevelopmental psychologyClinical psychologySocial supportQualitative researchPregnancySocial psychologySociology

Abstract

fetched live from OpenAlex

Persistent intense anger is indicative of postpartum distress, yet maternal anger has been little explored after childbirth. Using grounded theory, we explained how and why mothers develop intense anger after childbirth and the actions they take to manage their anger. Twenty mothers of healthy singleton infants described their experiences of anger during the first two postpartum years. Mothers indicated they became angry when they had violated expectations, compromised needs, and felt on edge (e.g., exhausted, stressed, and resentful), particularly around infants' sleep. Mothers described suppressing and/or expressing anger with outcomes such as conflict and recruiting support. Receiving support from partners, family, and others helped mothers manage their anger, with more positive outcomes. Women should be screened for intense anger, maternal-infant sleep problems, and adequacy of social supports after childbirth. Maternal anger can be reduced by changing expectations and helping mothers meet their needs through social and structural supports.

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)0.022
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.224
GPT teacher head0.533
Teacher spread0.309 · 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 designQualitative
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

Citations9
Published2022
Admission routes2
Has abstractyes

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