“Someone to talk to”: A qualitative study of oncology trainees' experience of mentorship around moral distress
Bibliographic record
Abstract
BACKGROUND: Moral distress is an intrinsic part of healthcare, particularly prevalent in oncology practitioners. Previous studies have suggested mentorship may play a role in combatting moral distress; however, there is a lack of good evidence aimed at understanding trainees' experience with either mentorship or moral distress, including the intersection between the two. METHODS: We conducted a single-centre study in the hermeneutic phenomenological approach at a Canadian academic cancer centre. Six semi-structured interviews with senior oncology trainees were conducted and analysed according to the interpretive profiles hermeneutic phenomenological approach. FINDINGS/RESULTS: Key findings include the idea that trainees do find mentorship valuable and helpful in navigating moral distress, which is described as common and inevitable, with a number of triggers and factors identified. However, a mentorship relationship must involve mutual respect, understanding, and honesty in order to be valuable. Additionally, engaging in open, honest discussions with mentors, particularly more senior individuals, is seen as a risk-benefit balance by trainees; vertical mentors bring more wisdom and experience, but may also have a greater impact on a trainee's future. CONCLUSION: This thought-provoking study highlights mentorship as a potential method to combat the troubling phenomenon of moral distress in oncology trainees.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".