Maternal and infant predictors of attendance at Neonatal Follow‐Up programmes
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Neonatal Follow-Up (NFU) programmes provide health services for families of infants at high risk of developmental problems following difficult or extremely premature birth: yet, up to 30% of families do not attend these programmes with their infants. METHODS: The study objective was to determine maternal and infant factors that predicted attendance at NFU programmes. Utilizing Andersen's Behavioural Model of Health Services Use, a prospective two-phase multi-site descriptive cohort study was conducted in three Canadian Neonatal Intensive Care Units (NICU) that refer to two affiliated NFU programmes. In Phase 1, 357 mothers completed standardized questionnaires that addressed maternal and infant factors, prior to their infants' NICU discharge. In Phase 2, attendance at NFU was followed at three time points over a 12-month period. Factors of interest included predisposing factors (e.g. demographic characteristics and social context); enabling factors (e.g. social support, travel distance, and income); and infant illness severity (i.e. needs factors). Multivariate logistic regression was used to estimate the odds ratio for each independent factor. RESULTS: Mothers parenting alone, experiencing higher levels of worry about maternal alcohol or drug use, or at greater distances from NFU were less likely to attend. Mothers experiencing higher maternal stress at the time of the infant's NICU hospitalization were more likely to attend NFU. No infant factors were predictive of NFU attendance. CONCLUSIONS: Mothers at risk of not attending NFU programmes with their infants require better identification, triage, referral and additional support to promote engagement with NFU programmes and improved quality of life for their high-risk infants.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it