Implementing <i>Essential Coaching for Every Mother</i> during the COVID‐19 pandemic: A pre‐post intervention study
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this project was to evaluate the preliminary impact of Essential Coaching for Every Mother on maternal self-efficacy, social support, postpartum anxiety, and postpartum depression. The secondary objective was to explore the acceptability of the Essential Coaching for Every Mother program provided during the COVID-19 pandemic. METHODS: A prospective pre-post study was conducted with first-time mothers in Nova Scotia, Canada, between July 15 and September 19, 2020. Participants completed a self-report survey at enrollment (after birth) and 6 weeks postpartum. Various standardized measures were used, and qualitative feedback on the program was also collected. Paired t tests were carried out to determine changes from baseline to follow-up on psychosocial outcomes, and qualitative feedback was analyzed through thematic analysis. RESULTS: A total of 88 women enrolled. Maternal self-efficacy increased between baseline (B) and follow-up (F) (B: 33.33; F: 37.11, P = 0.000), whereas anxiety (STAI) declined (B: 38.49; F: 34.79, P = 0.004). In terms of acceptability, 89% of participants felt that the number of messages was just right, 84.5% felt the messages contained all the information they needed relative to caring for a newborn, and 98.8% indicated they would recommend this program to other new mothers. CONCLUSIONS: Essential Coaching for Every Mother may play a role in increasing maternal self-efficacy and decreasing anxiety, although future work with a control group is needed to delineate the true effects of the program. Overall, mothers were satisfied with the Essential Coaching for Every Mother program and would recommend it for other mothers, during the COVID-19 pandemic and beyond.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".