Use of the pragmatic-explanatory continuum indicator summary tool in low- and middle-income country settings: systematic review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,108 | 0,370 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,010 | 0,008 |
| Bibliométrie | 0,021 | 0,020 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».