Mother–Infant Interaction During Postpartum Depression: A Metaphor Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".