The Case for Assessing and Reporting on Facilitator Fidelity: Introducing the Fidelity of Implementation in Parenting Programs Guideline
Bibliographic record
Abstract
The sizeable body of evidence indicating that parenting programs have a positive impact on children and families highlights the potential public health benefits of their implementation on a large scale. Despite evidence and global attention, beyond the highly controlled delivery of parenting programs via randomized trials, little is known about program effectiveness or how to explain the poorer results commonly observed when implemented in community settings. Researchers, practitioners, and policymakers must work together to identify what is needed to spur adoption and sustainment of evidence-based parenting programs in real-world service systems and how to enhance program effectiveness when delivered via these systems. Collecting, analyzing, and using facilitator fidelity data is an important frontier through which researchers and practitioners can contribute. In this commentary, we outline the value of assessing facilitator fidelity and utilizing the data generated from these assessments; describe gaps in research, knowledge, and practice; and recommend directions for research and practice. In making recommendations, we describe a collaborative process to develop a preliminary guideline-the Fidelity of Implementation in Parenting Programs Guideline or FIPP-to use when reporting on facilitator fidelity. Readers are invited to complete an online survey to provide comments and feedback on the first draft of the guideline. Supplementary Information: The online version contains supplementary material available at 10.1007/s43477-023-00092-5.
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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 | 0.188 | 0.511 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.032 | 0.048 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".