When is the evidence sufficiently supportive of real‐world application? Evidence‐based practices, open science, clinical readiness level
Bibliographic record
Abstract
Abstract Evidence‐based interventions are the standard for school psychology practice. Yet, how do professionals know when research scope, relevance, transparency, and quality are ready for real‐world application? There remain questions as to exactly how these core concepts of evidence‐based practices (EBPs) are realized. A discussion on whether psychological science can be relied on to deliver real‐world practices related to the coronavirus (COVID‐19) pandemic led IJzerman and colleagues to develop a rubric to evaluate research for real‐world application called evidence readiness level. This model is adapted for school psychologists' use in evaluating and implementing research for clinical practice. Clinical readiness level is a rubric that is designed to narrow the research‐to‐practice gap, provide criteria for EBPs, and specify the value of a scientist‐practitioner model of school psychology.
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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 | 0.059 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".