Developing Requirements for a Standardized System to Return Individual Research Results Back to Study Participants: Narrative Review
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.209 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".