Institutional, Motivational, and Resource Factors Influencing Health Scientists' Data-Sharing Behaviours
Bibliographic record
Abstract
This study proposes a composite model of data sharing to examine what determines health scientists' behaviours drawing on institutional, motivational, and resource perspectives. The proposed model was developed considering institutional theory and the theory of planned behaviour. In addition, resource utilization measures were also combined into the research model. Using a national researcher pool, the Community of Scientists' Scholar Database, the analysis included a total of 207 survey responses. Partial least-squares structural equation modelling was performed to evaluate the causal relationship among the data sharing study measures. Findings suggest that regulative pressure by journal publishers and the availability of data repositories was found to be significantly related to data-sharing behaviour. Three motivational factors—perceived career benefit, perceived career risk, and perceived effort—were also found to have a significant influence on attitude toward data sharing, which has a significant relationship with data-sharing behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.026 | 0.090 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".