Predictors of Infant Care Competence Among Mothers With Postpartum Depression
Bibliographic record
Abstract
Background/objective: Postpartum depression is linked to decreased quality mother-infant interactions and long-term negative impacts on children’s behavior and health. Infant care competence may be reduced by postpartum depression and other maternal or environmental variables. Thus, the objective of this study was to explain factors that contribute to perceived infant care competence among mothers with postpartum depression. Methods: Multiple regression analysis and correlational analysis were conducted to study associations between the predictors (depression severity, social support, child development, family functioning) and the outcome of perceived infant care competence among a peer support intervention study for mothers with postpartum depression (n = 55). Results: Child development, specifically communication ( P = .04), gross ( P = .00) and fine ( P = .00) motor skills, problem solving ( P = .00), and personal-social development ( P = .01), explained maternal perceptions of responsiveness, an aspect of infant care competence. The best-fit model was obtained for the responsiveness subscale, in which 37% of the variance was explained by mothers’ reports of infants’ fine motor skills ( P = .000) and nurturance ( P = .039) as an aspect of social support and family functioning ( P = .078). Conclusions: Recognition of the importance of infant development to perceived infant care competence, particularly mothers’ perceptions of infant responsiveness, may offer targets for intervention. Helping mothers identify infant cues and milestones that signal infant responsiveness may be beneficial. Moreover, social support and family functioning may be targets for intervention to promote perceived infant care competence in mothers affected by 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".