Current and Future Trends in Life Sciences Training: Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Every year, the life science field spends billions of dollars on educational activities worldwide. The continuing professional development of employees, especially in this field, encompasses great challenges. Emerging technologies appear to offer opportunity, but relatively little research has been done on the effectiveness of pedagogies and tools that have been used in the life sciences, and even less research has been devoted to understanding the potential power of emerging options that might determine the field's future. OBJECTIVE: In collaboration with the Life Sciences Trainers & Educators Network (LTEN), this study investigated the current state of the pedagogies and tools currently adopted by corporate training professionals in the life sciences as well as the professionals' perceptions of the impacts of emerging technologies on training. METHODS: This study adopted a mixed methods approach that included a survey and a follow-up interview. The survey consists of 18 broad questions with 15 subquestions in each of the five specific sectors of the life sciences field. Interviews were conducted by phone and lasted approximately 40 minutes, covering 18 questions designed to follow-up on findings from the survey items. RESULTS: Both survey and interview results indicated that the professionals were not satisfied with the status quo and that training and education in this field need to change. Most of the techniques and tools currently used have been used for some time. The professionals surveyed were not satisfied with the current techniques and tools and did not find them cost-effective. In addition, the respondents pictured the future of training in this field to be more engaging and effective. CONCLUSIONS: This is the first study in a series designed to better understand education and training in the life sciences on a macro level, in order to build a foundation for progress and evolution of the future landscape. Next steps involve developing strategies for how to extend this vision throughout individual organizations.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".