Psychometric Testing of a Scale to Measure Family Resilience Among Parents of Preterm Infants in the NICU
Bibliographic record
Abstract
OBJECTIVE: To evaluate the psychometric properties of the French-Canadian Communication, Organization, and Intrafamilial Beliefs in Neonatology (COCINL) NICU family resilience scale. DESIGN: Scale validation design. SETTING: Two urban level III NICUs in Canada. PARTICIPANTS: Participants included 88 parents of preterm infants who were hospitalized for more than 14 days. METHODS: We administered the COCINL scale along with validated questionnaires of psychological distress and of self-reported perception of family resilience. Psychometric testing included internal consistency, inter-rater reliability among couples, construct, divergent and exploratory criterion validation. RESULTS: We found high internal consistency for each subscale (⍺ = 0.80 to 0.91) and for the total scale (⍺ = 0.94). Interrater reliability among 11 couples demonstrated moderate agreement, with an ICC of 0.54. Strong correlations between subscales (r = 0.73 to 0.85) supported the internal structure, however, not by exploratory factor analysis. Divergent validation was supported by moderate correlations with psychological distress scores (r = 0.24). Exploratory sensitivity and specificity analysis indicated that the COCINL scale aligns with participants' perception of family resilience (AUC = 0.75). CONCLUSION: We found the psychometric properties of the COCINL scale to be adequate for measuring NICU family resilience for research purposes. The COCINL scale provides an assessment of family resilience during the NICU hospitalization that can be used to guide nursing interventions to promote parents' mental health in a family-centered care approach.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".