A cluster randomized clinical trial to evaluate the effectiveness of the Implementation of Infant Pain Practice Change (ImPaC) Resource to improve pain practices in hospitalized infants: a study protocol
Bibliographic record
Abstract
BACKGROUND: Hospitalized infants undergo multiple painful procedures daily. Despite the significant evidence, procedural pain assessment and management continues to be suboptimal. Repetitive and untreated pain at this vital developmental juncture is associated with negative behavioral and neurodevelopmental consequences. To address this knowledge to practice gap, we developed the web-based Implementation of Infant Pain Practice Change (ImPaC) Resource to guide change in healthcare professionals' pain practice behaviors. This protocol describes the evaluation of the intervention effectiveness and implementation of the Resource and how organizational context influences outcomes. METHODS: An effectiveness-implementation hybrid type 1 design, blending a cluster randomized clinical trial and a mixed-methods implementation study will be used. Eighteen Neonatal Intensive Care Units (NICUs) across Canada will be randomized to intervention (INT) or standard practice (SP) groups. NICUs in the INT group will receive the Resource for six months; those in the SP group will continue with practice as usual and will be offered the Resource after a six-month waiting period. Data analysts will be blinded to group allocation. To address the intervention effectiveness, the INT and SP groups will be compared on clinical outcomes including the proportion of infants who have procedural pain assessed and managed, and the frequency and nature of painful procedures. Data will be collected at baseline (before randomization) and at completion of the intervention (six months). Implementation outcomes (feasibility, fidelity, implementation cost, and reach) will be measured at completion of the intervention. Sustainability will be assessed at six and 12 months following the intervention. Organizational context will be assessed to examine its influence on intervention and implementation outcomes. DISCUSSION: This mixed-methods study aims to determine the effectiveness and the implementation of a multifaceted online strategy for changing healthcare professionals' pain practices for hospitalized infants. Implementation strategies that are easily and effectively implemented are important for sustained change. The results will inform healthcare professionals and decision-makers on how to address the challenges of implementing the Resource within various organizational contexts. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03825822. Registered 31 January 2019.
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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 | 0.416 | 0.526 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".