Implementation and Evaluation of the Premature Infant Pain Profile-revised (PIPP-R) e-Learning Module for Assessing Pain in Infants
Bibliographic record
Abstract
OBJECTIVES: The Premature Infant Pain Profile-revised (PIPP-R) is a well-established measure for infant pain assessment. The aim of this study was to evaluate the implementation and clinical utility of the PIPP-R electronic learning (e-Learning) module to promote standardized health care training for nurses. MATERIALS AND METHODS: A descriptive mixed-methods study was conducted in 2 tertiary Neonatal Intensive Care Units in Canada. Nurses were recruited and asked to complete the PIPP-R e-Learning Module and evaluate it. A 26-item questionnaire was used to describe nurse demographics and clinical experience and to evaluate implementation success (ie, acceptability, feasibility, usability) and clinical utility. RESULTS: In all, 98 nurses from 2 settings in Central and Eastern Canada participated; most were registered nurses highly experienced in neonatal nursing care. The majority had received previous training on the PIPP-R (61.2%) and routinely used it in practice (67.4%). They considered the e-Learning module as acceptable and feasible as it was easy to access (94.9%) and to navigate (94.8%). Content was considered clear (98.9%) and met users' learning needs (99.0%). Nurses agreed that completing the module improved their understanding of neonatal pain (96.0%) and was clinically useful in improving their ability to assess pain in neonates (97.9%). The module was accessed primarily from work settings (77.8%) using desktop computers (49.0%) or tablets (28.0%) and was usually completed in a single session (75.7%). DISCUSSION: Nurses' evaluation of the PIPP-R e-Learning module was overwhelmingly positive. The module was perceived as easy to implement, clinically useful, and was considered as a promising online educational tool. Further testing in clinical practice is needed to build on the results of this study and support the importance of dissemination of this module for standardized training purposes.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".