Open Science Practices Among Authors Published in Complementary, Alternative, and Integrative Medicine Journals: An International, Cross-Sectional Survey
Bibliographic record
Abstract
Abstract Background Open science practices aim to increase transparency in research and increase research availability through open data, open access platforms, and public access. Due to the increasing popularity of complementary, alternative, and integrative medicine (CAIM) research, our study aims to explore current open science practices and perceived barriers among CAIM researchers in their own respective research articles. Methods We conducted an international cross-sectional online survey that was sent to authors that published articles in MEDLINE-indexed journals categorized under the broad subject of “Complementary Therapies” or articles indexed under the MeSH term “Complementary Therapies”. Articles were extracted to obtain the names and emails of all corresponding authors. 8,786 researchers were emailed our survey, which included questions regarding participants’ familiarity with open science practices, their open science practices, and perceived barriers to open science in CAIM with respect to participants’ most recently published article. Basic descriptive statistics was generated based on the quantitative data. Results The survey was completed by 292 participants (3.32% response rate). Results indicate that the majority of participants were “very familiar” (n = 83, 31.68%) or “moderately familiar” (n = 83, 31.68%) with the concept of open science practices while creating their study. Open access publishing was the most familiar to participants, with 51.96% (n = 136) of survey respondents publishing with open access. Despite participants being familiar with other open science practices, the actual implementation of these practices was low. Common barriers participants experienced in implementing open science practices include not knowing where to share the study materials, where to share the data, or not knowing how to make a preprint. Conclusions Although participants responded that they were familiar with the concept of open science practices, the actual implementation and uses of these practices were low. Barriers included a lack of overall knowledge about open science, and an overall lack of funding or institutional support. Future efforts should aim to explore how to implement methods to improve open science training for CAIM researchers.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".