The implementation of infant pain practice change resource to improve infant procedural pain practices: a hybrid type 1 effectiveness-implementation study
Bibliographic record
Abstract
ABSTRACT: Implementation of infant pain practice change (ImPaC) is a multifaceted web-based resource to support pain practice change in neonatal intensive care unit (NICU). We evaluated the (1) intervention effectiveness and (2) implementation effectiveness of ImPaC using a hybrid type 1 effectiveness-implementation study (ie, cluster randomized controlled trial and longitudinal descriptive study). Eligible level 2 and 3 Canadian NICUs were randomized to intervention (INT) or waitlisted to usual care (UC) for 6 months. We assessed the number of painful procedures, proportion of procedures accompanied by valid assessment and evidence-based treatment, and pain intensity to determine intervention effectiveness using intention-to-treat (ITT) and wait-list (WL) analyses. Implementation feasibility and fidelity were explored. Twenty-three NICUs participated (12 INT, 11 UC). Thirty infants/NICU were included in the ITT (INT = 354, UC = 325) and the WL (INT = 678, UC = 325) analyses. In the ITT analysis, the average number of painful procedures/infant/day was lower in the INT group [2.62 (±3.47) vs 3.85 (±4.13), P < 0.001] than in the UC group. Pain assessment was greater in the INT group (34.7% vs 25.5%, P < 0.001) and pain intensity scores were lower [1.47 (1.25) vs 1.86 (1.97); P = 0.029]. Similarly, in the WL analysis, there were fewer painful procedures/infant/day [3.11 (±3.98) vs 3.85 (±4.13), P = 0.003] and increased pain assessment (30.4% vs 25.5%, P = 0.0001) and treatment (31.2% vs 24.0%, P < 0.001) in the INT group. Feasibility and implementation fidelity were associated with improved clinical outcomes.
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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 | 0.078 | 0.019 |
| 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; both teacher heads agree on what is shown here.
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".