Maternal and infant predictors of attendance at Neonatal Follow‐Up programmes
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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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| 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".