CAR Survey of Patterns and Perspectives on Multidisciplinary Team Rounds in Canada
Bibliographic record
Abstract
PURPOSE: This study aimed to assess the experiences and challenges faced by Canadian radiologists participating in Multidisciplinary Team rounds (MDT), with a focus on demographics, meeting characteristics, preparation processes, and perceptions of workload and compensation. METHODS: The Canadian Association of Radiologists constituted a working group which developed a 35-question survey that was distributed to 1958 radiologists and radiology trainees across Canada. The survey garnered 129 complete responses, for a response rate of 6.6%. RESULTS: Respondents predominantly practiced in academic settings (65.9%) and had subspecialty training (96.1%). The majority reported that MDT rounds lasted 30 to 60 minutes and discussed 6 to 10 cases. Most radiologists (62.8%) were the sole presenters of imaging. Preparation time was often limited, with only 6.2% having dedicated time for preparation. 59.8% of respondents reported receiving additions to caseloads within the 24-hour period prior to the meetings. While 93.8% valued the opportunity for interaction with colleagues, 93.8% felt inadequately compensated for their efforts by their practices, while 92.3% felt inadequately compensated by their province. CONCLUSIONS: While most radiologists indicated adequate time for discussion and meaningful clinical engagement during rounds, many highlighted repeated challenges such as last-minute case additions, lack of protected preparation time, and technological barriers. Systemic barriers also play a role and include lack of provincial remuneration and workforce issues which in turn impact individual workload pressures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".