Predictors of Early Parenting Self-efficacy
Bibliographic record
Abstract
Background: Parenting self-efficacy has been identified as one determinant of positive parenting. The literature is inconsistent regarding the predictors of parenting self-efficacy, and there is limited evidence regarding these predictors in the early postpartum period. Objectives: To determine the factors predictive of parenting self-efficacy at 12 to 48 hr after childbirth and at 1 month postpartum. Method: Six-hundred fifty-two women were recruited consecutively from the postpartum units of two general hospitals on Prince Edward Island, Canada. Data were collected at 12 to 48 hr postpartum using self-report and chart review. On the basis of scoring positive or negative on their childbirth perceptions, 175 of these mothers were assigned to two cohorts. They were visited at home at 1 month postpartum, where data were collected using self-report. Results: Using multiple logistic regression, greater parenting self-efficacy at 12 to 48 hr after childbirth was predicted by multiparity and single marital status and correlated with positive perception of the birth experience, higher general self-efficacy, and excellent partner relationship. Greater parenting self-efficacy at 1 month was predicted by age ≤30 years and multiparity and correlated with excellent partner relationship and maternal perception of infant contentment. Discussion: Birth perception is a correlate of parenting self-efficacy that is modifiable; therefore, nurses have an opportunity to strive to create a positive birth experience for all women to enhance their early parenting self-efficacy. Nurses can also consider assessing women at risk for suboptimal parenting self-efficacy and intervene through teaching, support, and parenting self-efficacy boosting interventions.
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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 | 0.001 | 0.006 |
| 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) | 0.003 | 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".