Complementary, Alternative, and Integrative Medicine Researchers’ Practices and Perceived Barriers Related to Open Science: An International, Cross-Sectional Survey
Bibliographic record
Abstract
Abstract Introduction and Objective Open science (OS) aims to make the dissemination of knowledge and the research process transparent and accessible to everyone. With the increasing popularity of complementary, alternative, and integrative medicine (CAIM), our objective was to assess practices and perceived barriers towards OS among CAIM researchers. Methods We conducted an anonymous online survey of researchers who published in journals listed in Scopus containing the words “complementary”, “alternative”, or “integrative” medicine in their names. We emailed 6040 researchers our purpose-built electronic survey after extracting their email address from one of their publications in our sample of journals. We questioned their familiarity with different OS concepts, along with their experiences and challenges engaging in these practices over the last 12 months. Results The survey was completed by 392 researchers (6.5% response rate, 97.1% completion rate). Most respondents were CAIM researchers familiar with the overall concept of OS, indicated by those actively publishing open access (OA) (n=244, 76.0%), registering a study protocol (n=148, 48.0%), and using reporting guidelines (n=181, 59.0%) in the past 12 months. Preprinting, sharing raw data, and sharing study materials were less popular. A lack of funding was reported as the greatest barrier to publishing OA by most respondents (n=252, 79.0%), and that additional funding is the most significant incentive in applying more OS practices to their research (n=229,72.2%). With respect to preprinting barriers, 36.3% (n=110) participants believed there are potential harms in sharing non-peer-reviewed work and 37.0% (n=112) feared preprinting would reduce the likelihood of their manuscript being accepted by a journal. Respondents were also concerned about intellectual property control regarding sharing data (n=94, 31.7%) and research study materials (n=80, 28.7%). Conclusions Although many participants were familiar with and practiced aspects of OS, many reported facing barriers relating to lack of funding to enable OS and perceived risks of revealing research ideas and data prior to publication. Future research should explore interventions, such as increasing CAIM researchers’ funding and educational resources to implement OS practices.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".