Oncologists’ Locus of Control, Compassion Fatigue, Compassion Satisfaction, and the Mediating Role of Helplessness
Bibliographic record
Abstract
The oncology setting may give rise to significant feelings of helplessness among oncologists via patients’ inevitable deaths or suffering. The current study examines whether and how oncologists’ sense of control (locus of control; LOC) influences their compassion fatigue and satisfaction. Methods: Seventy-three oncologists completed the following questionnaires: the Professional Quality of Life scale; Levenson’s Internal, Powerful Others, and Chance scale; the Guilt Inventory, State Guilt subscale; and the Learned Helplessness scale. Results: Oncologists reported high levels of secondary traumatic stress and burnout and moderate levels of compassion satisfaction. A positive association between oncologists’ external LOC and compassion fatigue, and a negative association between oncologists’ internal LOC and compassion fatigue, were found. Helplessness, but not guilt, had a mediating role in these associations. Internal LOC was also positively associated with compassion satisfaction. Conclusions: The current study highlights oncologists as a population at risk of experiencing compassion fatigue and emphasizes oncologists’ locus of control as a predisposition that plays a role in the development of this phenomenon. Additionally, the cognitive as well as the emotional aspects of control were found to be important factors associated with compassion fatigue.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".