Systematic review of predictors of infant care competence among women with postpartum depression
Bibliographic record
Abstract
Postpartum depression (PPD) is a serious illness that affects mothers worldwide. The symptoms of PPD such as low mood and fatigue undermine the quality of mothers’ interactions with their children, likely explaining the less than optimal development of children of mothers with PPD. In this way, PPD reduces mothers’ Infant Care Competence (ICC), that is, mothers’ perceived and performed infant caregiving ability. ICC is similar to other concepts such as maternal competence; however, ICC is specific in its focus on a mother’s perceptions of her infant care abilities. Knowledge of predictors of ICC in the context of PPD would inform interventions for mothers with PPD to increase maternal caregiving quality, preventing negative long-term effects on children’s development. Thus, an integrative systematic review was completed to determine predictors of ICC (both performed and perceived abilities) in the context of PPD. Six electronic databases were searched (MEDLINE, PubMed, PsycINFO, Cumulative Index to Nursing and Allied Health Literature [CINAHL], SocINDEX, and the Cochrane Library) for relevant studies that met search criteria. Twenty-one eligible articles were obtained. Results revealed variables that explained ICC in the context of maternal PPD including: depression severity and timing of depressive symptoms, social support, maternal adversities, infant characteristics, as well as demographic variables such as education and income. Overall, this review provides insight into common explanatory variables of ICC that could be used to target interventions in the postpartum period to promote maternal caregiving abilities, and ultimately children’s development and health in the context of PPD.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".