Best practices for supporting researchers’ mental health in emotionally demanding research across academic and non-academic contexts
Bibliographic record
Abstract
PURPOSE: Researcher mental health in emotionally demanding research (EDR) has been recognized as important, but research to date has often been limited to academic research contexts, qualitative research, or single disciplines. The aim of this study was to identify best practices to promote researchers' mental health in EDR across academic and non-academic contexts. METHODS: Twenty-six researchers experienced in EDR (aged 33-64) were recruited across sectors and disciplines (e.g. sport psychology, palliative care, conflict resolution). Semi-structured online 2:1 interviews were conducted between October 2023 and January 2024. The co-designed interview guide asked questions on best practices at individual and contextual levels when undertaking EDR. Interviews were analysed through reflexive thematic analysis. RESULTS: Three themes were generated: (1) the need for a psychologically informed research culture; (2) actions and principles in the immediate research environment; and (3) researcher boundaries with the research, others, and oneself. Underlying mechanisms across themes included tailored, iterative and flexible, and collaborative. CONCLUSIONS: A shift is needed towards a more psychologically informed research culture to support mental health in EDR. Findings have implications for research organizations, conference organizers, and funders as greater resources are needed for researchers in EDR, regardless of method, discipline, or sector.
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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 | 0.462 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.024 | 0.042 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.008 | 0.032 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".