An Investigation of Factors Influencing the Postponement of the Use of Distributed Research Networks in South Korea: Web-Based Users’ Survey Study
Bibliographic record
Abstract
Background Distributed research networks (DRNs) offer researchers the advantage of using various big data sets that are difficult to access and use. In addition, since the data are not physically exposed to the outside, it is possible to conduct research using medical data safely without data exposure. However, researchers still have difficulties and are concerned about using DRNs. Few studies involving DRNs have been conducted from the user’s viewpoint. Therefore, it is necessary to look at DRNs from the researcher’s point of view and find ways to facilitate the active use of DRNs. Objective This study aimed to identify the factors that made researchers hesitate to use DRNs and to derive a method to facilitate active DRN use. Methods We conducted a web-based survey of people working in the medical fields, such as hospitals and universities. We used 131 respondents’ data from a survey from December 6 to 17, 2021. We conducted multiple regression analyses to determine the factors affecting the postponement of using DRNs. In addition, 2 independent sample t tests were conducted to analyze the difference between the 2 groups according to the following factors: organization, gender, experience with DRNs, length of the research career, position, and age. Results Performance risk (t5=2.725, P=.007) and workload from DRNs (t5=3.543, P=.001) were significantly associated with users’ postponement of DRN use. Researchers working at hospitals were found to feel more burdened by DRN use than researchers working at universities (t129=1.975, P=.05). It was also found that women perceived a higher privacy risk of DRNs than men (t129=–2.303, P=.02) and that those who had experience using DRNs delayed their use less than those without experience (t129=–4.215, P<.001). Conclusions It is necessary to simplify the research and approval processes to reduce the performance risk and workload of research using DRNs. To optimize the process, DRN providers should develop a way to improve users’ experiences. More user-friendly functionalities should be developed from the researcher's point of view. It is necessary to continuously promote effective functionalities for DRNs to reduce concerns about privacy risks. This study identified the concerns of DRN users in terms of DRN use and suggested ways to actively use DRNs. The derived results can be reflected in planning and developing DRNs. Our research will be helpful to prepare an activation plan for DRNs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".