French Translation, Adaptation, and Initial Validation of the Nurses’ Attitudes and Perceptions of Pain Assessment in Neonatal Intensive Care Questionnaire (NAPPAQ)
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to translate, adapt and conduct initial psychometric validation of the French version of the Nurses' Attitudes and Perceptions of Pain Assessment in neonatal intensive care Questionnaire (NAPPAQ) developed by Polkki in 2010. BACKGROUND: Assessing nurses' perceptions, attitudes and knowledge about pain management in preterm infants is important to improve neonatal practices. METHODS: A sample of French-speaking nurses (n = 147) from Quebec and France working in neonatal intensive care was selected to validate the 46-item questionnaire. A French translation of the NAPPAQ, which includes Part I and II, was undertaken prior to its administration. The FIPM questionnaire was added as a Part III. Internal consistency and instrument structure were examined using Cronbach's alphas, inter-item and inter-scale correlations and exploratory factor analysis. RESULTS: The NAPPAQ-FIPM is divided into three parts. Part I of the French version had a Cronbach's alpha of 0.64 and was composed of five factors. Part II had good total internal consistency (0.79) and adequate structure, established by inter-item correlations. Part III had good total internal consistency (0.76), and factor analysis findings suggested the presence of five factors. CONCLUSIONS: The NAPPAQ-FIPM can be used for research purposes. Parts II and III obtained adequate psychometrics results. However, further refinement of Part I could improve its content and internal structure.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".