Use of the theoretical domains framework and behaviour change wheel to develop a novel intervention to improve the quality of multidisciplinary cancer conference decision-making
Bibliographic record
Abstract
BACKGROUND: Multidisciplinary Cancer Conferences (MCCs) are prospective meetings involving cancer specialists to discuss treatment plans for patients with cancer. Despite reported gaps in MCC quality, there have been few efforts to improve its functioning. The purpose of this study was to use theoretically-rooted knowledge translation (KT) theories and frameworks to inform the development of a strategy to improve MCC decision-making quality. METHODS: A multi-phased approach was used to design an intervention titled the KT-MCC Strategy. First, key informant interviews framed using the Theoretical Domains Framework (TDF) were conducted with MCC participants to identify barriers and facilitators to optimal MCC decision-making. Second, identified TDF domains were mapped to corresponding strategies using the COM-B Behavior Change Wheel to develop the KT-MCC Strategy. Finally, focus groups with MCC participants were held to confirm acceptability of the proposed KT-MCC Strategy. RESULTS: Data saturation was reached at n = 21 interviews. Twenty-seven barrier themes and 13 facilitator themes were ascribed to 11 and 10 TDF domains, respectively. Differences in reported barriers by physician specialty were observed. The resulting KT-MCC Strategy included workshops, chair training, team training, standardized intake forms and a synoptic discussion checklist, and, audit and feedback. Focus groups (n = 3, participants 18) confirmed the acceptability of the identified interventions. CONCLUSION: Myriad factors were found to influence MCC decision making. We present a novel application of the TDF and COM-B to the context of MCCs. We comprehensively describe the barriers and facilitators that impact MCC decision making and propose strategies that may positively impact the quality of MCC decision making.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".