Exploring Social Identity Clusters and NICU Outcomes in the Context of Alberta Family Integrated Care
Bibliographic record
Abstract
OBJECTIVE: To explore variations in maternal and infant outcomes among clusters of mother-infant dyads in the NICU characterized by intersecting social identity characteristics. DESIGN: Secondary exploratory analysis of data from a cluster randomized controlled trial conducted from December 2015 to July 2018. SETTING: Ten Level II NICUs in six cities across Alberta, Canada. PARTICIPANTS: weeks gestation. METHODS: We used two-step cluster analysis to identify clusters based on maternal ethnicity, education, age, and annual family income. We employed multiple regression models to examine whether cluster membership was associated with infant length of stay, maternal psychosocial distress, and parenting self-efficacy at discharge, controlling for relevant infant and maternal characteristics and hospital setting (urban vs. regional). RESULTS: We identified four mother-infant dyad clusters: (1) younger, lower-education, lower-income White mothers; (2) older, higher-education, higher-income BIPOC (Black, Indigenous, or people of color) mothers; (3) diploma-educated, highest-income White mothers; and (4) university-educated, highest-income White mothers. Although cluster membership was not associated with maternal outcomes, infants of mothers in Cluster 1 had shorter lengths of stay compared with those in Cluster 4. Hospital setting was a predictor of length of stay and parenting self-efficacy. CONCLUSION: Findings highlight the relevance of social identity and hospital setting in shaping NICU outcomes and support the need for equity-informed neonatal care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".