How can diagnostic assessment programs be implemented to enhance inter-professional collaborative care for cancer?
Bibliographic record
Abstract
BACKGROUND: Inter-professional collaborative care (ICC) for cancer leads to multiple system, organizational, professional, and patient benefits, but is limited by numerous challenges. Empirical research on interventions that promote or enable ICC is sparse so guidance on how to achieve ICC is lacking. Research shows that ICC for diagnosis could be improved. Diagnostic assessment programs (DAPs) appear to be a promising model for enabling ICC. The purpose of this study was to explore how DAP structure and function enable ICC, and whether that may be associated with organizational and clinical outcomes. METHODS: A case study approach will be used to explore ICC among eight DAPs that vary by type of cancer (lung, breast), academic status, and geographic region. To describe DAP function and outcomes, and gather information that will enable costing, recommendations expressed in DAP standards and clinical guidelines will be assessed through retrospective observational study. Data will be acquired from databases maintained by participating DAPs and the provincial cancer agency, and confirmed by and supplemented with review of medical records. We will conduct a pilot study to explore the feasibility of estimating the incremental cost-effectiveness ratio using person-level data from medical records and other sources. Interviews will be conducted with health professionals, staff, and referring physicians from each DAP to learn about barriers and facilitators of ICC. Qualitative methods based on a grounded approach will be used to guide sampling, data collection and analysis. DISCUSSION: Findings may reveal opportunities for unique structures, interventions or tools that enable ICC that could be developed, implemented, and evaluated through future research. This information will serve as a formative needs assessment to identify the nature of ongoing or required improvements, which can be directly used by our decision maker collaborators, and as a framework by policy makers, cancer system managers, and DAP managers elsewhere to strategically plan for and implement diagnostic cancer services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".