Setting the tone: perspectives on the role of the team in promoting a healthy and inclusive research environment
Bibliographic record
Abstract
Purpose This study aims to answer the following research questions: What factors contribute to faculty, postdocs, research staff and graduate students feeling part of a healthy and inclusive team environment? Design/methodology/approach The authors conducted student, postdoctoral fellow, staff and faculty focus groups to solicit perceptions on the characteristics of healthy and inclusive research teams, and how research team members can contribute to shaping this environment. Focus groups were semistructured and guided by an appreciative inquiry approach. Thematic analysis was used to summarize and categorize findings across focus groups and to understand how these themes contributed to the overall research questions. Findings The authors conducted 11 focus groups that were comprised of 48 different individuals: 30 graduate students (6 focus groups), 6 faculty members (2 focus groups), 6 staff members (2 focus groups) and 6 postdoctoral fellows (1 focus group). Themes that were discussed included collaboration and clarity on role definition; effective communication; cultivating safe relationships; promoting and modeling work–life balance; and supporting professional development in these areas. Originality/value This study reinforces the role that research teams can have on how graduate students, postdoctoral fellows, staff and faculty experience the research environment. The authors also identified a number of themes and factors that can be used to develop training initiatives to facilitate healthy research team environments.
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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 | 0.063 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.022 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.005 | 0.011 |
| 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".