Conditions for production of interdisciplinary teamwork outcomes in oncology teams: protocol for a realist evaluation
Bibliographic record
Abstract
BACKGROUND: Interdisciplinary teamwork (ITW) is designed to promote the active participation of several disciplines in delivering comprehensive cancer care to patients. ITW provides mechanisms to support continuous communication among care providers, optimize professionals' participation in clinical decision-making within and across disciplines, and foster care coordination along the cancer trajectory. However, ITW mechanisms are not activated optimally by all teams, resulting in a gap between desired outcomes of ITW and actual outcomes observed. The aim of the present study is to identify the conditions underlying outcome production by ITW in local oncology teams. METHODS: This retrospective multiple case study will draw upon realist evaluation principles to explore associations among context, mechanisms and outcomes (CMO). The cases are nine interdisciplinary cancer teams that participated in a previous study evaluating ITW outcomes. Qualitative data sources will be used to construct a picture of CMO associations in each case. For data collection, reflexive focus groups will be held to capture patients' and professionals' perspectives on ITW, using the guiding question, 'What works, for whom, and under what circumstances?' Intra-case analysis will be used to trace associations between context, ITW mechanisms, and patient outcomes. Inter-case analysis will be used to compare the different cases' CMO associations for a better understanding of the phenomenon under study. DISCUSSION: This multiple case study will use realist evaluation principles to draw lessons about how certain contexts are more or less likely to produce particular outcomes. The results will make it possible to target more specifically the actions required to optimize structures and to activate the best mechanisms to meet the needs of cancer patients. This project could also contribute significantly to the development of improved research methods for conducting realist evaluations of complex healthcare interventions. To our knowledge, this study is the first to use CMO associations to improved empirical and theoretical understanding of interdisciplinary teamwork in oncology, and its results could foster more effective implementation in clinical practice.
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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 | 0.203 | 0.225 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.058 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".