Transdisciplinary is the new multidisciplinary: why and how can healthcare workers and managers make the shift?
Bibliographic record
Abstract
Introduction: Demand for healthcare services is increasing due to the ageing population and events such as the COVID-19 pandemic. Healthcare workers and managers can respond by shifting from multidisciplinary to transdisciplinary teams, where multiple services are integrated and designed around the healthcare needs of individuals and patient groups. Background: Transdisciplinary teams provide the formal structures for healthcare professionals to blur professional boundaries, share and acquire new skills and knowledge, support each other, and provide high quality patient care. Instead of organising healthcare services in silos of professional groupings, transdisciplinary teams place the patient at the centre of care. Reorganising teams around the patient can reduce duplication of healthcare services, the number of healthcare professionals delivering services, and occasions of service. Healthcare professionals gain skills, time, and the ability to support each other, which are qualities of a flexible and capable workforce. Aims: In this submission, we show healthcare workers and managers how co-designed transdisciplinary teams can be translated into clinical practice. We aim to explain why and how to shift from multidisciplinary to transdisciplinary teams by 1) providing clinical evidence demonstrating how transdisciplinary teams can improve person-centred care and the healthcare workforce; and 2) sharing practical learnings when planning a transdisciplinary team. Methods/engagement: At the Mater Hospital Brisbane, assessment processes were reorganised around patients experiencing stroke symptoms. A transdisciplinary assessment was co-designed by allied health professionals, nurses, and managers where tasks and questions were integrated from occupational therapy, physiotherapy, speech pathology, social work, and nursing. A clinical mixed methods study evaluated the transdisciplinary assessment compared to usual multidisciplinary assessment. Patients and staff were also consulted during the clinical study via surveys and fortnightly meetings. Results/impact: All occupational therapists and physiotherapists completed transdisciplinary competency training, which improved staff availability and flexibility to comprehensively assess patients. Use of a transdisciplinary assessment resulted in a time saving of approximately 53 minutes per assessment, which indicated improved workforce productivity and capacity. The time saved could be reallocated to increasing rehabilitation time, or to an extra occasion of service to the same or other patients. Additionally, staff reported that the transdisciplinary assessment “saves time and helps staff manage clinical workloads by reallocating resources across teams” and resulted in an “agile workforce”. Learnings/next steps: To co-design and shift from a multidisciplinary to transdisciplinary team, stakeholder consultation was imperative to ensure comprehensive planning and barrier identification. Addressing barriers when shifting to a transdisciplinary team involved behaviour change from staff. For example, a lack of confidence in the skills of another professional, or a lack of willingness to share skills could impede transdisciplinary efforts. To negotiate these barriers, it was essential to establish clear competency training requirements and educate staff on the purpose of transdisciplinary teams for improving patient care and workforce capacity. To translate transdisciplinary teams into other healthcare settings internationally, we propose the practice guidelines: 1) key stakeholders should be consulted to co-design the transdisciplinary team, identify and address implementation barriers; 2) competency training is required; and 3) staff education on transdisciplinary teams is required.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".