Difficult Patient Loss and Physician Culture for Oncologists Grieving Patient Loss
Bibliographic record
Abstract
BACKGROUND: While caring for critically ill and terminal patients can elicit grief symptoms in health care professionals, few studies have examined oncologists' grief over patient loss using a qualitative approach to inquiry. OBJECTIVES: To explore what makes patient loss difficult for oncologists and to explore the context in which these losses were occurring. METHOD: Twenty oncologists were interviewed at three oncology centers in Canada about their experiences of grief over patient loss. Exclusion criteria included never having lost a patient in their care and being unable to speak English. Data was analyzed using the grounded theory method. RESULTS: Oncologists found patient loss particularly difficult for relational reasons including instances where they felt close to patients and their families, when they had a transference to the patient, when patients died young, when they had long-term patients, and when deaths were unexpected. Contextual reasons included when patients and their families were unprepared for death, had unrealistic expectations about cure, when excessive treatments were perceived to be used, when physicians were blamed for the loss, or when families were chaotic or had high needs. Findings further revealed that these losses were occurring within a physician culture that had a stigma around death and dying, viewed emotion as weakness, was focused on cure, and was gendered. CONCLUSIONS: Effective interventions to help oncologists cope with grief must identify the expectation gaps between physicians and patients when it comes to end-of-life care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".