Emotional Vulnerability in Researchers Conducting Trauma-Triggering Research
Bibliographic record
Abstract
Qualitative researchers prioritize rapport-building to ensure safety of research participants and validity of data collected. Although there is extensive literature about prioritizing the safety and emotional well-being of research participants, much less has been written on the topic of researcher vulnerability with lack of consideration for researcher safety within ethics approval applications. The authors present a reflexive account of a research project involving interviews with young people aged 15 to 30 in Toronto, Canada who had firearm related charges. The methodological, ethical issues, and research burnout and vulnerability that arose due to the shared lived experience between the principal researcher and the research participants are discussed. Overall, the article explores the complexities and nuances involved when conducting research with topics that may be trauma-triggering and can contribute to researcher burnout and compassionate fatigue. It is argued that researchers are not immune to these risk factors and due to such exposure may experience depression and other negative side effects. Series of suggestions are outlined to reduce harm exposure for researchers and to improve how they can better be supported to cope and heal from conducting trauma-triggering research before, during, and after completion of a research project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.411 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.031 | 0.071 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".