Input of Psychosocial Information During Multidisciplinary Team Meetings at Medical Oncology Departments: Protocol for an Observational Study
Bibliographic record
Abstract
BACKGROUND: Multidisciplinary team meetings (MDTMs) have become standard practice in oncology and gained the status of the key decision-making forum for cancer patient management. The current literature provides evidence that MDTMs are achieving their intended objectives but there are also indications to question the positive impact of MDTMs in oncology settings. For cancer management to be patient-centered, it is crucial that medical information as well as psychosocial aspects-such as the patients' living situation, possible family problems, patients' mental state, and patients' perceptions and values or preferences towards treatment or care-are considered and discussed during MDTMs. Previous studies demonstrate that failure to account for patients' psychosocial information has a negative impact on the implementation of the treatment recommendations formulated during MDTMs. Few empirical studies have demonstrated the predominant role of physicians during MDTMs, leading to the phenomenon that medical information is shared almost exclusively at the expense of psychosocial information. However, more in-depth insight on the underlying reasons why MDTMs fail to take into account psychosocial information of cancer patients is needed. OBJECTIVE: This paper presents a research protocol for a cross-sectional observational study that will focus on exploring the barriers to considering psychosocial information during MDTMs at medical oncology departments. METHODS: This protocol encompasses a cross-sectional comparative case study of MDTMs at medical oncology departments in Flanders, Belgium. MDTMs from various oncology subspecialties at inpatient medical oncology departments in multiple hospitals (academic as well as general hospitals) are compared. The observations focus on the "multidisciplinary oncology consultation" (MOC), a formally regulated and financed type of MDTM in Belgian oncology since 2003. Data are collected through nonparticipant observations of MOC-meetings. Observational data are supplemented with semi-structured individual interviews with members of the MOC-meetings. RESULTS: The protocol is part of a larger research project on communication and multidisciplinary collaboration in oncology departments. Results of this study will particularly focus on the input of psychosocial information during MDTMs. CONCLUSIONS: The concept of an MDTM should not merely be a group of care professionals who mostly work independently and occasionally liaise with one another. Interventions aiming to enhance the input of psychosocial information are crucial to ensure that MDTMs can benefit from their diverse membership to achieve their full potential. The findings from this study can be used to design nonclinical and organizational interventions that enhance multidisciplinary decision-making in oncology.
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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 | 0.046 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".