Evaluation of the Premature Infant Pain Profile-Revised (PIPP-R) e-Learning Module
Bibliographic record
Abstract
BACKGROUND: Electronic health (e-health) learning is a potential avenue to educate health professionals about accurately using infant pain assessment tools, although little is known about the impact of e-health interventions on clinical competence. PURPOSE: To evaluate whether an e-health learning module for teaching the accurate use of the Premature Infant Pain Profile-Revised (PIPP-R) pain assessment tool results in immediate and sustained competency to assess infant pain. METHODS: Neonatal intensive care unit (NICU) nurses who participated in a larger study across 2 tertiary NICUs in Canada examining the implementation and clinical utility of the PIPP-R e-learning module completed 2 follow-up evaluations at 1 week and 3 months. Participants were asked to view a video recording of an infant undergoing a painful procedure and to assess the infant's pain intensity response using the PIPP-R measure. Immediate and sustained competency was assessed via interrater consensus of participant-reported PIPP-R scores compared with those of an experienced trained coder. RESULTS: Of the 25 eligible nurses, 22 completed 1-week and 3-month follow-up evaluations. At the 1-week follow-up, 84% of nurses scored the video accurately compared with 50% at 3 months. Behavioral pain indicators were more likely to be scored incorrectly than physiological indicators. IMPLICATIONS FOR PRACTICE: Follow-up training after completion of the initial e-learning module training may improve competency related to the clinical use of the PIPP-R tool to assess infant pain over time. IMPLICATIONS FOR RESEARCH: Additional study regarding the need and timing of e-health training to optimize sustained competency in infant pain assessment is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".