French translation and preliminary psychometric validation of a skin‐to‐skin contact instrument for nurses (SSC‐F)
Bibliographic record
Abstract
PURPOSE: To translate and conduct the preliminary psychometric validation of a skin-to-skin contact instrument in French (SSC-F) with a sample of nurses from Quebec and France working in neonatal intensive care units. METHODS: The 20 items of the SSC instrument containing four subscales (knowledge, attitudes and beliefs, training and education and implementation), developed by Vittner et al. (2017), was translated into French. The methodological steps used for psychometric validation included assessment of the item and subscale normality distributions, assessment of reliability using internal consistency, and assessment of validity using inter-item and inter-scale correlations and principal component analysis. RESULTS: The preliminary psychometric validation showed that all four subscales of the French version had adequate internal consistency (0.61-0.77), supporting the calculation of a total score for each subscale based on the English version of the instrument. The structural validity was supported by principal component analysis findings. PRACTICE IMPLICATIONS: Based on the findings of the preliminary psychometric validation of our study, the SSC-F instrument could be used in research with French-speaking neonatal nurses in Western countries, but gathering more evidence about its reliability and validity is warranted for clinical practice.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".