Use of the pragmatic-explanatory continuum indicator summary tool in low- and middle-income country settings: systematic review
Bibliographic record
Abstract
OBJECTIVES: To systematically review and characterize the literature on using the pragmatic-explanatory continuum indicator summary (PRECIS) tools in low- and middle-income countries (LMICs), focusing on successes, challenges, and potential improvements to enhance applicability across diverse settings. STUDY DESIGN AND SETTING: A systematic search of PubMed to identify peer-reviewed articles applying PRECIS tools to LMIC-based research. Data extraction focused on trial characteristics, modifications, and use of PRECIS tools. Narrative synthesis was used to outline successes, challenges, and recommendations. RESULTS: A total of 40 articles met the selection criteria. The PRECIS tools were mostly (n = 39, 97.5%) used for purposes other than trial design. Significant variation was seen in methods of use and reporting. Most (n = 32, 80%) used PRECIS-2, valued for its reliability, ability to quantify pragmatism, assess trial design, and identify research gaps. Challenges included the tools' subjectivity, absence of information needed for scoring, interpretation of scores, and application to non-Western contexts and multinational trials. Recommendations for improvement included refining scoring criteria, translating guidance, and developing additional educational resources. CONCLUSION: The PRECIS tools have successfully supported research globally and are perceived as reliable research tools with multiple strengths. Further guidance and refinement would enable consistent application and reporting, particularly as the tools have frequently been used for purposes other than their original intention. Most challenges were similar to high-income settings; however, translation and application of the tools to traditional medicine, international trials, and research-naïve settings were highlighted as LMIC-focused issues requiring consideration. PLAIN LANGUAGE SUMMARY: The pragmatic-explanatory continuum indicator summary (PRECIS) tools were created to help researchers design better studies. The tools were developed mainly by researchers from developed Western nations. Therefore, it is possible that the PRECIS tools are not as relevant to other places. To help improve the usefulness of the tools in all settings, our team wanted to learn from the experiences of people who had already used PRECIS in low- and middle-income countries. We systematically searched for academic papers on this topic published before May 2022 and found 40 relevant articles. The articles showed that the PRECIS tools had been successfully used in many, often unexpected, ways to support research. Some researchers struggled with using the tool to assess research conducted by others, as relevant information was not available. Researchers recommended translating the tools to other languages and asked for more guidance to use the tool in specific circumstances, such as Chinese herbal medicine and large international research projects. Advice on the best ways to use the PRECIS tools and report the findings would also be beneficial. We share these findings to help those designing the next version of the tool make it useful for researchers working in all parts of the world.
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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 | 0.108 | 0.370 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".