A Comparison of Fidelity Implementation Frameworks Used in the Field of Early Intervention
Bibliographic record
Abstract
Implementation fidelity is the degree of compliance with which the core elements of program or intervention practices are used as intended. The scientific literature reveals gaps in defining and assessing implementation fidelity in early intervention: lack of common definitions and conceptual framework as well as their lack of application. Through a critical review of the scientific literature, this article aims to identify information that can be used to develop a common language and guidelines for assessing implementation fidelity. An analysis of 46 theoretical and empirical papers about early intervention implementation, published between 1998 and 2018, identified four conceptual frameworks, in addition to that of Dane and Schneider. Following analysis of the conceptual frameworks, a four-component conceptualization of implementation fidelity (adherence, dosage, quality and participant responsiveness) is proposed.
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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 | 0.301 | 0.458 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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; 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".