Optimising Scopes of Practice and Team‐Based Collaborative Care Through Task‐Shifting and Task‐Sharing in Mental Health—A Collaborative Patient Care (<scp>CPC</scp>) Initiative
Bibliographic record
Abstract
Team-based collaborative models utilise a broad range of healthcare clinicians that practise at the top of their licence, while proactively redistributing shared work through task-shifting, offering meaningful ways to contribute to patient care while ensuring high-quality outcomes. A mental health hospital in Canada embarked on a Collaborative Patient Care (CPC) initiative to optimise skill and skill mix of interdisciplinary inpatient teams. Implementation science, specifically the Exploration, Preparation, Implementation and Sustainment (EPIS) Framework, was utilised to guide the project. Following qualitative and quantitative syntheses, analyses and stakeholder engagement, CPC re-imagined team-based care by restructuring one clinical team of recreational therapy and introduced 26 new positions to infuse across the organisation, including new disciplines of rehabilitation assistant, geriatric physiotherapist, occupational therapists and bachelor of social work roles, while recruiting for more child and youth workers, recreational therapists, secretaries, psychologists, behavioural therapists and personal support workers. Scopes of work were defined to support differences (i.e., between Registered Nurses (RN) and Registered Practical Nurses (RPN)) while team responsibilities were designed to support shared practices. An educational upskilling plan was implemented to support unregulated and regulated clinicians to perform at the level of the new model. At the time of this paper, CPC is immersed in its Sustainment stage. CPC represents a comprehensive plan aimed at enhancing patient care through service efficiencies and optimising resource allocation. It is anticipated that the implementation of CPC will contribute to a shared vision for a better future where patients (and families) receive the right care at the right time by the right clinician.
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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 | 0.050 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".