VALIDATION OF A PROCESS EVALUATION CHECKLIST TO MEASURE INTERVENTION IMPLEMENTATION FIDELITY
Bibliographic record
Abstract
Objectives The EPIC intervention (Lee, 2002), is a multifaceted knowledge translation intervention that combines evidence and continuous quality improvement to change health professional practices. As components of this intervention are complex, there is a need to evaluate the intervention process by assessing the extent to which the intervention was implemented as planned (i.e. fidelity) and the feasibility of implementation. The objective of this study is to develop and validate the Process Evaluation Checklist (PEC) to assess the fidelity and feasibility of implementing the EPIC intervention in a Neonatal Intensive Care Unit (NICU). Methods Face validity of the PEC was determined by co-investigators of the CIHR Team in Children’s Pain (Stevens, et al. 2006). To establish content validity, domains of the process evaluation of the PEC will be sent electronically to experts who have participated in the EPIC intervention. Quantification of content validity will be achieved using a content validity index (CVI). Results Based on feedback regarding face validity of the PEC, items in the checklist that were confusing were re-worded, clarified, refined, reduced and arranged in a suitable sequence. Comments were minor and focused on the structure/layout of the questions. Results from the content validity ratings from experts will be used to further refine the PEC. Conclusions Implementation of the EPIC intervention using a validated process evaluation measure will provide information about the fidelity and feasibility of processes when delivering the EPIC intervention and will allow future studies to replicate the EPIC intervention in a variety of settings and conditions.
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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 | 0.336 | 0.422 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".