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Record W2147247140 · doi:10.3109/01612840.2012.656824

Unveiling New Dimensions: A Hermeneutic Exploration of Perinatal Mood Disorder and Infant Feeding

2012· article· en· W2147247140 on OpenAlexaff
Joan Margaret Humphries, Carol McDonald

Bibliographic record

VenueIssues in Mental Health Nursing · 2012
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBreastfeedingMental healthMoodPostpartum depressionMedicinePsychologyNursingDepression (economics)Face (sociological concept)Breast feedingPsychiatryPediatricsPregnancy

Abstract

fetched live from OpenAlex

In this hermeneutic study, six women from the local Perinatal Mental Health Program were interviewed about their experiences with infant feeding. None of the women in the study were breastfeeding their infants. The research question centered on their experience of formula feeding with a view to gaining better insight about the issues that women face when feeding practices do not conform to best-practice (i.e., breastfeeding) promotional standards. We also considered the needs of nurses working in the mental health setting, who may face conflicting recommendations concerning the treatment of a mental health crisis in the presence of current infant feeding best-practice guidelines. Our findings support concerns that current guidelines overlook the special needs of women who live with perinatal mood disorder. We speculate that breastfeeding challenges may present a risk for postpartum depression in women who are biologically vulnerable. The need for ongoing assessment for emerging depression among women who are experiencing breastfeeding challenges is identified. The importance of deepened understanding among mental health nurses is highlighted.

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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0100.014
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.000

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 head0.382
Teacher spread0.346 · 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 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

Citations11
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueIssues in Mental Health NursingSame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207