Designing provider-focused implementation trials with purpose and intent: introducing the PRECIS-2-PS tool
Bibliographic record
Abstract
BACKGROUND: First articulated by Schwartz and Lellouch (1967), randomized controlled trials (RCTs) can be conceptualized along a continuum from more explanatory to more pragmatic. The purpose and intent of the former is to test interventions under ideal contexts, and the purpose and intent of the latter is to test interventions in real-world contexts. The PRagmatic Explanatory Continuum Indicator Summary-2 (PRECIS-2) is a validated tool that helps researchers make decisions about the elements of the trial to match the overall purpose and intent of the trial along the continuum. The PRECIS-2 tool has guided the design of hundreds of RCTs. However, a few aspects of the tool would benefit from greater clarity, including its application to provider-focused implementation trials rather than patient-focused intervention trials. MAIN TEXT: We describe the newly developed PRECIS-2-Provider Strategies (PRECIS-2-PS) tool, an extension of the PRECIS-2 tool, which has been adapted for trials testing provider-focused strategies. We elaborate on nine domains that can make a provider-focused trial more explanatory or more pragmatic, including eligibility, recruitment, setting, implementation resources, flexibility of provider strategies, flexibility of intervention, data collection, primary outcome, and primary analysis. We detail the complementary roles that researchers and stakeholders play in the trial design phase, with implications for generalizability of trial results to the contexts in which they are intended to be applied. CONCLUSIONS: The PRECIS-2-PS tool is designed to help research and practice teams plan for provider-focused trials that reflect the overall intent and purpose of the trial. The tool has potential to help advance the science of provider-focused strategies across a range of trials, with the ultimate goal of facilitating the adoption, integration, and sustainability of provider-focused strategies outside the context of 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 | 0.449 | 0.644 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".