"Building through the Grief": Vicarious Trauma in a Group of Inner-City Family Physicians
Bibliographic record
Abstract
BACKGROUND: Vicarious trauma is an understudied phenomenon among Canadian family physicians. OBJECTIVE: This phenomenological study set out to explore the experiences of a group of inner-city family physicians caring for women using illicit drugs. METHODS: Ten family physicians working in Toronto and Ottawa, Canada, participated in in-depth interviews. The data were analyzed using an iterative and interpretive process. RESULTS: The first major theme emerging from the data analysis was the emotional impact of the work. Participants shared the challenges, sorrows, and joys they experienced as they struggled to care for their patients. The sub-themes identified were as follows: tragedy and death, difficult behaviors, and isolation from mainstream medical community. The second major theme identified was coping strategies. Participants were open, thoughtful, and eloquent as they reflected on the three primary coping strategies reported: adaptation and evolution of practice style, teamwork, and modification of expectations. CONCLUSIONS: Participants, narratives of loss, grief, and compassion were consistent with vicarious trauma and therefore participants risked developing compassion fatigue--a specific form of burnout. These are new and important findings. Further research exploring vicarious trauma as a possible contributor to burnout among family physicians is warranted.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".