[Study of two indicators of difficulty in initiating maternal breastfeeding : maternal fatigue and suboptimal infant breastfeeding behaviour].
Bibliographic record
Abstract
The problems with setting up breastfeading are common and are associated with a heightened risk of weening. The main motives brought up by the mothers for early weening were fatigue and the suboptimal infant breastfeeding behaviour (SIBB). The goal of this study is to describe the fatigue levels of breastfeading women who gave birth to healthy full term babies in a hospital setting and the SIBB. This study also examines the relation between these two variables. This descriptive correlational and cross-sectional study was conducted with breasfeeding women who gave birth to healthy, full term, single babies at the CHU in Moncton between June and September 2013. Fatigue Continuum Form and Infant Breastfeeding Assesement Tool were each used to measure fatigue and SIBB. Analysis was done using Spearman’s correlation coefficient (r) and bivariate associations where measured using the chi-square test (χ2). Sixty-four mother child dyads were included in this study. 64.1 % of mothers experienced moderate fatigue. Maternal fatigue was associated with parity (p=0,003), the mothers’ level of education (p=0,043), the child’s birth weight (p=0,035), the delay before introducing the breast (p=0,001) and the frequency of feedings on D0 (p=0,037). The prevalence of SIBB was 57.8 % on day 0 (D0) and 25 % on day 1 (D1). SIBB is associated with a frequency of less than eight feedings in the first 24 hours (D0 (p=0,001) and D1(p=0,003)), to the primiparity (D1) (p=0,046), nipple types (D1) (p=0,010), previous breastfeeding experience (D1) (p=0,046). Skin on skin contact in the delivery room (D1) (p=0,041) and the delay before the first introduction to the breast (D1) (p=0,027). À significant correlation was noted between maternal fatigue and SIBB on D0 (r=0,196 ; p=0,029) and between SIBB on D0 and on D1 (r=0,661 ; p<0.001). A special supervision of mother-child dyads that are at risk is necessary in order to benefit from extra support during hospital stays and following their discharge from the hospital.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".