Perioperative chemotherapy for bladder cancer: A qualitative study of physician knowledge, attitudes, and behaviour
Bibliographic record
Abstract
INTRODUCTION: Use of chemotherapy for muscle-invasive bladder cancer (MIBC) is known to be low. To understand factors driving practice we use the Theoretical Domains Framework (TDF) to identify barriers and enablers of chemotherapy use. METHODS: A convenience sample of Canadian urologists, medical oncologists (MOs), and radiation oncologists (ROs) participated in individual, semi-structured, one-hour telephone interviews. An interview guide was developed using the TDF to assess potential barriers and enablers of chemotherapy use. Interviews were recorded and transcribed. Two investigators independently identified barriers and enablers and assigned them to specific themes. Participant recruitment continued until saturation. RESULTS: A total of 71 physicians were invited to participate and 34 (48%) agreed to be interviewed: 13 urologists, 10 MOs, and 11 ROs. We identified the following barriers to the use of chemotherapy (relevant TDF domains in parentheses): 1) belief that the benefits of chemotherapy are not clinically important (beliefs about consequences); 2) inadequate multidisciplinary collaboration (environmental context and resources); 3) absence of "champions" advocating the use of chemotherapy (social and professional role); and 4) a lack of organizational clarity/policy regarding the referral process (environmental context and resources). The predominant enablers identified included: 1) "champions" who believe in the value of chemotherapy (social and professional role); 2) urologists who refer all patients to MO (behavioural regulation; memory, attention, and decision-making); and 3) system-level factors, including automatic multidisciplinary referral (environmental context and resources). CONCLUSIONS: We have identified several system-level factors associated with delivery of chemotherapy. Behaviour change interventions should optimize multidisciplinary care of patients with MIBC. PATIENT SUMMARY: Despite the fact that chemotherapy before or after surgery improves survival of patients with bladder cancer, several studies have shown that many patients in routine practice are not treated. In this study, we identify important system-level and physician-level factors that must be considered in efforts to improve patient care.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".