Developing Requirements for a Standardized System to Return Individual Research Results Back to Study Participants: Narrative Review
Notice bibliographique
Résumé
Background: The increasing prevalence of smart devices has created vast amounts of untapped data, presenting new opportunities for data sharing across various fields, such as environmental sciences, health management, and astrophysics. While a significant portion of the public is willing to donate personal data, we need to better understand how to obtain information about which data assets a person may hold and the risks, benefits, and potential uses of this data exchange mechanism. Developing a trusted data-sharing platform may increase participants' willingness to donate data and researchers' ability to return personalized results from research findings. Objective: This study aimed to develop a preliminary list of core requirements, which can be used to develop design recommendations for standardizing the return of individual research results to study participants across research disciplines. Methods: We conducted a narrative literature review of existing platforms used to return research results to study participants. The search strategy included English-language articles published between May 2013 and May 2023. Concepts related to returning, disseminating, and sharing research results were searched for in (1) published research reports on Web of Science and MEDLINE, (2) gray literature, and (3) the bibliographies of included articles. Screening and data extraction were performed by 2 independent reviewers using Covidence. Inclusion criteria required that the study (1) included human participants, (2) returned information based on data collected from or by participants, (3) was published in English, and (4) included a description of a results-sharing system. Articles that met all 4 inclusion criteria were included in the review; articles that met the first 3 were also presented as supplementary articles. Results and requirements were synthesized thematically. Results: Overall, 6608 abstracts were screened, and 266 articles underwent full-text review to identify 8 articles describing the development and evaluation of 7 different return of results systems. In total, 7 of the 8 articles reported the use of multimodal dissemination methods, including a combination of physical documents, emails, phone calls, and digital platforms to support text and graphical data representations. One article outlined accessibility features to serve the specific participant population. None of the articles described in detail how results were or were not anonymized. A total of 4 studies relied on an expert or clinician to share results on behalf of the research team. Additional educational or contextual materials were included alongside results in four studies, including specific materials designed for follow-up with experts and clinicians. Participants were not hesitant to receive unfavorable results and instead aimed to incorporate such information into their lives via lifestyle changes, clinical intervention, or seeking community. Conclusions: Return of results systems should support multiple modes of dissemination for text-based results. Additional educational and lay-language materials are helpful for participants to understand and use information gained from receiving results.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,104 | 0,141 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,006 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,009 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».