Supporting Patients With Incurable Cancer: Backup Behavior in Multidisciplinary Cross-Functional Teams
Bibliographic record
Abstract
Caring for patients with incurable cancer presents unique challenges. Managing symptoms that evolve with changing clinical status and, at the same time, ensuring alignment with patient goals demands specific attention from clinicians. With care needs that often transcend traditional service provision boundaries, patients who seek palliation commonly interface with a team of providers that represents multiple disciplines across multiple settings. In this case study, we explore some of the dynamics of a cross-disciplinary approach to symptom management in an integrated outpatient radiotherapy service model. Providers who care for patients with incurable cancer must rely on one another to secure delivery of the right services at the right time by the right person. In a model of shared responsibilities, flexibility in who does what and when can enhance overall team performance. Adapting requires within-team and between-team monitoring of task and function execution for any given patient. This can be facilitated by a common understanding of the purpose of the clinical team and an awareness of the particular circumstances surrounding care provision. Backup behavior, in which one team member steps in to help another meet an expectation that would otherwise not be fulfilled, is a supportive team practice that may follow naturally in high-functioning teams. Such team processes as these have a place in the care of patients with incurable cancer and help to ensure that individual provider efforts more effectively translate into improved palliation for patients with unmet needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".