Bearing witness to suffering: A reflection on the personal effects of conducting qualitative research with victims of trauma
Bibliographic record
Abstract
Qualitative researchers in the health sciences often engage with participants to collect information about their thoughts, feelings, and behaviours related to a particular research topic. When the topic under investigation involves a traumatic event or experience, listening to, or being exposed to those personal accounts, can carry a significant emotional cost for the mental health of the researcher who can develop secondary trauma. Yet there is limited exploration of the effects of participant trauma on qualitative researchers. This is the focus of the current paper. Objective: Our goal is to relate how we have approached, experienced, and worked with our partner organization and the goal of better understanding the experiences of frontline providers caring for residents with disabilities living in a community residential setting during a COVID-19 outbreak. Methods: Using reflection as a qualitative method, we describe our experiences as we undertake this important work. Results: As researchers, navigating sensitive, deeply felt experiences can be difficult. We share an interim offering of our processes and experiences garnering the insights and feelings of frontline providers who lived through the physical and emotional stress of a COVID-19 outbreak. It has required us to be nimble and resourceful, but most critically to be present. Several lessons emerge from this work such as preparing to address a difficult topic, creating a realistic work plan, and creating spaces for reflection in the research team in order to take care of our mental health. Conclusions: Every study is a story. Often the reader wants the final chapter – the conclusion. We aim to illuminate the path we are travelling, as for us it has been and is equally compelling.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".