Participant-Centric Initiatives and Medical Research: Scoping Review Protocol
Bibliographic record
Abstract
BACKGROUND: Significant advances in digital technologies have meant that health care data can be collected, stored, transferred, and analyzed for research purposes more easily than ever before. Participant-centric initiatives (PCI) are defined as "tools, programs, and projects that empower participants to engage in the research process" using digital technologies and have the potential to provide a number of benefits to both participants and researchers, including the promotion of public trust in medical research, improved quality of research, increased recruitment and retention, and improved health care delivery. OBJECTIVE: The main objective of this scoping review is to describe the extent and range of PCIs across the United Kingdom, United States, and Japan that are designed to facilitate medical research. METHODS: The methodological framework described by Levac et al will be applied to this scoping review. We will search electronic databases (MEDLINE, EMBASE, PsychINFO, Cumulative Index to Nursing, and Allied Health Literature and CiNii), grey literature sources, Internet search engines (Google and Bing), and hand search key journals and reference lists of relevant articles. All digital tools and programs will be eligible for inclusion if there is a description of key features and functions that fall within the parameters of a PCI. Only those that play a role in medical research will be included. RESULTS: Preliminary searches conducted in MEDLINE and EMBASE retrieved 1820 and 2322 results, respectively. The scoping review will be completed by January 2018. CONCLUSIONS: The scoping review will be the first to map the extent and range of PCIs currently available across the United Kingdom, United States, and Japan, and will be the first review to contribute to a better understanding of what PCIs patients may benefit from. Researchers and practitioners will be able to use information in this review as a guide for patients and also as a guide for the development of future tools and programs. The results will be disseminated through a peer-reviewed publication and conference presentations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.180 | 0.144 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.071 | 0.021 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".