Content Validity of a Process Evaluation Checklist to Measure Intervention Implementation Fidelity of the EPIC Intervention
Bibliographic record
Abstract
BACKGROUND: The Evidence-Based Practice Identification and Change (EPIC) intervention is a complex multifaceted knowledge translation strategy that combines the use of evidence and continuous quality improvement to change health care professional practices. However, there is no measure to evaluate the fidelity (degree to which the intervention was implemented as planned) of the EPIC intervention. AIM: To examine the content validity of the Process Evaluation Checklist (PEC), a newly developed measure to assess the fidelity of the EPIC intervention. METHODS: Eight health care professionals with experience in the delivery of the EPIC intervention rated the importance/relevance of items in assessing the scale/subscales of the PEC. A content validity index was computed for each item (I-CVI) and for each scale/subscale (S-CVI) in the measure. RESULTS: I-CVIs ranged from 0.6 to 1.0 and S-CVIs ranged from 0.3 to 1.0. Two items were eliminated, while nine items were retained. CONCLUSIONS: Content validity of the PEC was established. The PEC will be used to evaluate the implementation fidelity of the EPIC intervention in future trials.
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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 | 0.164 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".