Understanding Physicians’ Perceptions of Overuse of Health Services in Oncology
Bibliographic record
Abstract
Overuse rates in oncology are high, but areas of possible improvement exist for reducing it and improving quality of care. This study explores perceptions and experiences of oncologists in Israel regarding overuse of health services within oncology. In-depth, semistructured interviews were conducted focusing on causes of overuse, facilitators for reduction, and suggestions for improvement. Interviews were audio recorded, transcribed, coded, and thematically analyzed. Physicians reported patient-level causes including "well-informed" and "demanding" patients; physician-level causes including desire to satisfy patients, lack of confidence, time, and skills; and system-level causes like ease of access, and lack of alignment and coordination. Physicians can reduce overuse through patient dialogue, building trust and solidifying patient-physician relationships, and further reduce overuse with better teamwork. Improvements can be made through educational initiatives, and bottom-up solutions. Policy makers and decision makers should develop appropriate interventions addressing health service overuse, including improving patient education and instilling confidence and knowledge in physicians.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".