Open Science Practices in Behavioral Addictions: An Exploratory Survey
Bibliographic record
Abstract
Background: The field of behavioral addictions (BA) research addresses activity domains such as excessive gaming, gambling, and other online behaviors that influence public health policies. A failure to embrace open science practices may lead to concerns about the trustworthiness and reliability of its research outputs. This study explored the current use of open science practices among BA researchers, focusing on the adoption, underlying motivations, concerns, and support needs across seven specific open science practices.Methods: We distributed an exploratory survey through professional networks, conferences, and social media and received a final N = 83 (early career researcher (ECRs) N= 41). The survey covered six domains: general use, frequency, importance, engagement, concerns, and support needs related to open science practices.Results: Most respondents reported positive attitudes toward open science, with preregistration (75% of total N) and data sharing (65% of total N) as the most commonly used practices. Descriptively, ECRs placed greater importance on these practices than their established counterparts, suggesting a potential generational shift. ECRs primarily reported concerns about insufficient knowledge and fear of errors, while established researchers emphasized workload and a lack of incentives. Both groups highlighted the need for increased time, resources, institutional support, and training.Discussion: Although our findings are descriptive and limited by self-selection and sample bias, they offer initial insights into how open science is perceived and practised in the field. Sustained progress requires coordinated action from individuals, institutions, and professional societies in terms of knowledge transfer and incentives to ensure inclusive and equitable adoption of open science 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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".