Identifying barriers and enablers to rigorous conduct and reporting of preclinical laboratory studies
Bibliographic record
Abstract
Use of rigorous study design methods and transparent reporting in publications are 2 key strategies proposed to improve the reproducibility of preclinical research. Despite promotion of these practices by funders and journals, assessments suggest uptake is low in preclinical research. Thirty preclinical scientists were interviewed to better understand barriers and enablers to rigorous design and reporting. The interview guide was informed by the Theoretical Domains Framework, which is a framework used to understand determinants of current and desired behavior. Four global themes were identified; 2 reflecting enablers and 2 reflecting barriers. We found that basic scientists are highly motivated to apply the methods of rigorous design and reporting and perceive a number of benefits to their adoption (e.g., improved quality and reliability). However, there was varied awareness of the guidelines and in implementation of these practices. Researchers also noted that these guidelines can result in disadvantages, such as increased sample sizes, expenses, time, and can require several personnel to operationalize. Most researchers expressed additional resources such as personnel and education/training would better enable the application of some methods. Using existing guidance (Behaviour Change Wheel (BCW); Expert Recommendations for Implementing Change (ERIC) project implementation strategies), we mapped and coded our interview findings to identify potential interventions, policies, and implementation strategies to improve routine use of the guidelines by preclinical scientists. These findings will help inform specific strategies that may guide the development of programs and resources to improve experimental design and transparent reporting in preclinical research.
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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 | 0.642 | 0.732 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier 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".