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Record W2999314955 · doi:10.1177/0844562119897756

Mother–Infant Interaction During Postpartum Depression: A Metaphor Analysis

2020· article· en· W2999314955 on OpenAlexvenueno aff
Cheryl Tatano Beck

Bibliographic record

VenueCanadian Journal of Nursing Research · 2020
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
Fundersnot available
KeywordsPostpartum depressionTerminologyMetaphorQualitative researchPsychologyDepression (economics)Developmental psychologyQualitative analysisQualitative propertyClinical psychologyMedicineComputer sciencePregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: Reported in quantitative studies is the negative impact that postpartum depression can have on mother-infant bonding. Metaphors can enhance mothers' communication with their health-care providers that cannot be captured by medical terminology and provide mothers a different voice to explain their experiences interacting with their infants. PURPOSE: The aim of the study was to identify the metaphorical expressions used by women to describe their interactions with their infants during postpartum depression. METHODS: Secondary qualitative data analysis of three primary qualitative data sets of postpartum depression was conducted. The specific type of secondary qualitative analysis used was cross validation where multiple data sets from different studies are compared to expand the results of each individual study to make a more general claim. Data analysis involved using the Metaphor Identification Procedure. RESULTS: Identified were eight metaphors used by mothers to describe their interactions with their infants during their postpartum depression: a thief, a robot, enveloping fogginess, being at the races, an actor, an erupting volcano, skin crawling, and a wall. CONCLUSIONS: Being attentive to metaphors mothers use can provide a unique approach to helping nurses identify vulnerable mother-infant dyads during postpartum depression.

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)0.001
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 score0.040
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.113
GPT teacher head0.421
Teacher spread0.308 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Nursing ResearchSame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207