Maternal Role Adaptation Scale in Neonatal Intensive Care Units (MRAS: NICU): Development, Validation and Psychometric Tests
Bibliographic record
Abstract
BACKGROUND: Maternal role adaptation involves conceptualization and establishment of a responsible maternal role, which is characterized by a new identity and formation of mothering behaviors. Becoming a mother in intensive care unit is very different from becoming a mother with a term infant at home. The aim of the study was to develop a valid and reliable tool for assessment of maternal role adaptation of mothers with preterm neonates admitted to neonatal intensive care units. METHODS: This was an exploratory study which was conducted in 2 phase of qualitative and quantitative. A 90-item scale was developed after semi-structured interviews with 25 mothers and review of literature. After merging the similar items, it reduced to 45-item scale. Validity was determined through assessment of face, content and constructs validities, and reliability was confirmed through internal consistency and test-retest. RESULTS: Face validity led to elimination of 2 items, and further 8 items were eliminated through content validity index with cut-off point 0.79 and content validity ratio with cut-off point 0.42. Thus, the number of items reduced to 35-item. In exploratory factor analysis, 6 factors were identified that explained 54% of the variance. Construct validity led to elimination of 3 other items, and the final scale was developed with 32 items. Cronbach’s alpha and intra-class correlation coefficient were 0.77 and 0.81 respectively. CONCLUSION: The 32-item “Maternal role adaptation scale in mothers with preterm neonates admitted to neonatal intensive care units” (MRAS: NICU) is a valid and reliable tool.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".