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Stories of women involved in a postpartum depression peer support group

2012· article· en· W2141472129 on OpenAlexafffundabout
Phyllis Montgomery, Sharolyn Mossey, Sara Trillio Adams, Patricia Hill Bailey

Bibliographic record

VenueInternational Journal of Mental Health Nursing · 2012
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsLaurentian University
FundersHealth CanadaCanadian Mental Health AssociationCanadian Psychological AssociationOntario Trillium Foundation
KeywordsPostpartum depressionPeer supportSupport groupContext (archaeology)NarrativePsychologyParticipatory action researchPeer groupPostpartum periodDepression (economics)MedicineNursingDevelopmental psychologyPsychiatrySociologyPregnancy

Abstract

fetched live from OpenAlex

Living through postpartum depression (PPD) might lead women to seek a variety of support to re-establish their well-being, including a hybrid of traditional and non-traditional services. Within this mix, some women participate in peer groups; however, there is a paucity of research regarding their subjective experiences of engaging in this type of support. The purpose of this study was to describe how women talked about living through PPD in the context of a peer support group. This focused ethnography was a component of a larger participatory action study in northern Ontario, Canada. The seven members of a 5-week peer support group described their postpartum experiences through written, visual, and spoken stories. Using structural narrative analysis, stories about recovery were identified across the data. Three groups of recovery stories were labelled as illness, mothering wisdom, and mobilizing. The findings suggested that women actively sought and established a therapeutic space for PPD recovery with peers. As such, health-care providers are encouraged to acknowledge the merits and advocate for the multiple and diverse alliances women might require to actualize recovery.

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)0.004
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 score0.253
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.141
GPT teacher head0.473
Teacher spread0.332 · 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

Citations51
Published2012
Admission routes3
Has abstractyes

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