Pragmatic measures for implementation research: development of the Psychometric and Pragmatic Evidence Rating Scale (PAPERS)
Bibliographic record
Abstract
The use of reliable, valid measures in implementation practice will remain limited without pragmatic measures. Previous research identified the need for pragmatic measures, though the characteristic identification used only expert opinion and literature review. Our team completed four studies to develop a stakeholder-driven pragmatic rating criteria for implementation measures. We published Studies 1 (identifying dimensions of the pragmatic construct) and 2 (clarifying the internal structure) that engaged stakeholders-participants in mental health provider and implementation settings-to identify 17 terms/phrases across four categories: Useful, Compatible, Acceptable, and Easy. This paper presents Studies 3 and 4: a Delphi to ascertain stakeholder-prioritized dimensions within a mental health context, and a pilot study applying the rating criteria. Stakeholders (N = 26) participated in a Delphi and rated the relevance of 17 terms/phrases to the pragmatic construct. The investigator team further defined and shortened the list, which were piloted with 60 implementation measures. The Delphi confirmed the importance of all pragmatic criteria, but provided little guidance on relative importance. The investigators removed or combined terms/phrases to obtain 11 criteria. The 6-point rating system assigned to each criterion demonstrated sufficient variability across items. The grey literature did not add critical information. This work produced the first stakeholder-driven rating criteria to assess whether measures are pragmatic. The Psychometric and Pragmatic Evidence Rating Scale (PAPERS) combines the pragmatic criteria with psychometric rating criteria, from previous work. Use of PAPERS can inform development of implementation measures and to assess the quality of existing measures.
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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 | 0.428 | 0.679 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".