Risk of Secondary Distress for Graduate Students Conducting Qualitative Research on Sensitive Subjects: A Scoping Review of Canadian Dissertations and Theses
Bibliographic record
Abstract
Qualitative research, in the methods employed and topics explored, is emotionally demanding. While it is common for ethics protocols to protect research participants from emotional distress, the personal impact of emotional work on the researcher can often go unaddressed. Qualitative researchers, in particular graduate student researchers studying sensitive topics, are at risk of psychological effects. It is unclear, however, how this impact on the researcher is discussed in graduate student work and/or the steps taken to address this risk. To provide an overview of how impact on the researcher is considered in Canadian graduate student research, a comprehensive scoping review of dissertations was conducted. Less than 5% ( n = 11) of dissertations reviewed included a plan to mitigate psychological risk to the researcher—suggesting a need for further guidance on minimizing risk of emotional distress. The application of trauma and violence-informed principles to graduate supervision policy and practice is discussed as a promising harm mitigation strategy.
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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 | 0.109 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.037 | 0.044 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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 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".