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Enregistrement W7115678137 · doi:10.48448/ev62-sh58

Factors Associated with the Reproducibility of Health Sciences Research: A Systematic Review and Evidence and Gap Map

2025· other· W7115678137 sur OpenAlexaffabout

Notice bibliographique

RevueUnderline Science Inc. · 2025
Typeother
Langue
Domaine
Thématique
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésReproducibilityOperationalizationRelevance (law)Reliability (semiconductor)MEDLINEScientific evidenceQuality (philosophy)Empirical research

Résumé

récupéré en direct d'OpenAlex

Stephana Julia Moss,<sup>1</sup> Juliane Kennett,<sup>2</sup> Jeanna Parsons Leigh,<sup>3</sup> Niklas Bobrovitz,<sup>4</sup> Henry T. Stelfox<sup>5</sup> <h4>Objective </h4> To map the evidence for factors (eg, research practices) associated with the reproducibility of methods and results reported in health sciences research. <h4>Design </h4> Five bibliographic databases were searched from January 2000 to May 2023, followed by supplemental searches of high-impact journals and relevant records. We included health science records of observational, interventional, or knowledge synthesis studies reporting data on factors related to research reproducibility. Factors were operationalized as modifiable or nonmodifiable aspects of study conduct relating to individual-, study-, or institutional-level practices, methods, and processes that could impact the reproducibility of research methods or results.<sup>1</sup> Reproducibility was operationalized by 2 mutually exclusive categories: (1) methodological reproducibility (ie, the ability to exactly repeat the methods, including study procedures and data analysis) and (2) results reproducibility (ie, obtaining corroborating results using the same or similar methods).<sup>2</sup> We included studies that used surrogate measures for reproducibility (eg, type 1 or 2 error rates) if they (1) explicitly stated their aim to investigate the reproducibility of research and (2) rationalized their choice of surrogate measure.<sup>3</sup> Data were coded using inductive qualitative content analysis, and empirical evidence was synthesized with evidence and gap maps. Study risk of bias was assessed using the Quality in Prognostic Studies risk-of-bias tool. Statistical tests of the association between factors and reproducibility outcomes were summarized as reported in the included articles. <h4>Results </h4> Our review included 148 primarily biomedical and preclinical (n = 62) and clinical (n = 71) studies. Factors were classified into 12 modifiable (eg, sample size and power) and 3 nonmodifiable (eg, publication year) categories. Of 234 reported evaluations of factors, 76 (32%) assessed methodological reproducibility and 158 (68%) assessed results reproducibility. The most frequently reported factor was transparency and reporting (38 of 234 assessments [16%]). A total of 155 factors (66%) were evaluated for statistical associations with reproducibility outcomes (<b>Table 25-0858</b>). Statistical associations were most frequently conducted for analytical methods (24 of 26 reporting significance [92%]), sample size and power (21 of 23 reporting significance [91%]), and participant characteristics and study materials (10 of 12 reporting significance [83%]). Risk-of-bias assessments found low risk of bias for study participation, factor measurement, and statistical analysis, and high risk of bias for confounding. https://assets.underline.io/markdown_image/1/image/b16148b83c9aa6aaf32a839e2bc713d9.png <h4>Conclusions </h4> Our review identified a large body of literature consisting primarily of observational studies of factors associated with the reproducibility of health sciences research. The data suggest that reproducibility may be improved by implementing more stringent statistical testing procedures and thresholds, sample size and power calculations, and improved transparency and completeness of reporting. Experimental studies are needed to test interventions to improve reproducibility. Factors identified in this study with consistent observational support should be prioritized for experimentation. Factors that affect reproducibility in health and social care services and population and public health need to be identified given the paucity of data in these areas. <h4>References</h4> 1. Goodman SN, Fanelli D, Ioannidis JP. What does research reproducibility mean? <i>Sci Transl Med</i>. 2016;8(341):341ps12-341ps12. doi:10.1126/scitranslmed.aaf5027 2. Niven DJ, McCormick TJ, Straus SE, et al. Reproducibility of clinical research in critical care: a scoping review. <i>BMC Med</i>. 2018;16:1-12. doi:10.1186/s12916-018-1018-6 3. Clemens MA. The meaning of failed replications: a review and proposal. <i>J Econ Surveys</i>. 2017;31(1):326-342. doi:10.1111/joes.12139 <sup>1</sup>Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada, sj.moss@dal.ca; <sup>2</sup>Department of Critical Care Medicine, University of Calgary, Calgary, Alberta, Canada; <sup>3</sup>Faculty of Health, Dalhousie University, Halifax, Nova Scotia, Canada; <sup>4</sup>Department of Emergency Medicine, University of Calgary, Calgary, Alberta, Canada; <sup>5</sup>Faculty of Medicine &amp; Dentistry, University of Alberta, Edmonton, Alberta, Canada. <h4>Conflict of Interest Disclosures</h4> None reported. <h4>Funding/Support</h4> This work was funded by the Canadian Institutes of Health Research. <h4>Role of the Funder/Sponsor </h4> The funding body had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the abstract; or decision to submit the abstract for presentation.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,295
score de la tête « metaresearch » (Gemma)0,148
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesMétarecherche, Études des sciences et des technologies
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,937
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,2950,148
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,000
Bibliométrie0,0020,018
Études des sciences et des technologies0,0050,059
Communication savante0,0010,001
Science ouverte0,0040,002
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,404
Tête enseignante GPT0,461
Écart entre enseignants0,057 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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