Multidisciplinary Cancer Conferences: Exploring Obstacles and Facilitators to Their Implementation
Bibliographic record
Abstract
PURPOSE: Multidisciplinary cancer conferences (MCCs) provide an opportunity for health professionals to discuss diagnosis and treatment options to optimize patient management. The purpose of this study was to explore the barriers and facilitators in implementing MCCs in Canada. METHODS: This exploratory study used qualitative interviews and observation to explore the experiences of implementing MCCs in four hospitals in Ontario, Canada. Interviews were transcribed verbatim and analyzed using standard qualitative research methodology guided by grounded theory principles. RESULTS: Thirty-seven MCCs for gastrointestinal cancer were observed across three hospital sites, and 48 interviews were conducted among a range of clinical specialists and administrators. The dominant theme suggested that MCCs can most effectively be implemented if administrators and health professionals see value in MCCs, despite the time and effort required. A number of factors (eg, provincial policy, hospital administrative and clinician support, and an efficient MCC process) influenced whether MCCs were valued. CONCLUSION: Variation exists in the enthusiasm of health professionals and the administrative capacity of institutions regarding routine implementation of MCCs. A systematic implementation plan for MCCs is needed involving both cancer care providers and administrators.
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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 | 0.029 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".