Integrated models of care in Canadian hospitals: Findings from a multi-methods study
Bibliographic record
Abstract
Background: Canada historic approach to care delivery has led to a significant overburdening of the Canadian health workforce. As a result, the country is now facing significant HHR challenges. Federal and non-federal initiatives over the past 3 years have placed unanimous emphasis on implementing team-based models of care across all healthcare settings. Interprofessional team-based models of care (henceforth, MoC) are one potential approach to alleviating HHR burden. As hospitals make up one of the foci of HHR challenges, better understanding the state of interprofessional team-based models of care within hospital settings may improve the ability to make effective policy recommendations for healthcare quality improvement and system change. Approach: This exploratory project aimed to identify innovative MoC in pan-Canadian hospital settings while understanding how these models can support Canada HHR challenges and develop implementation considerations for scalability. An emergent design methodology was used to conduct 2 phases of data collection: ) a literature search to synthesize existing knowledge on characteristics of successful MoC and an environmental scan to identify exemplar cases of MoC across Canadian hospitals; and 2) subsequent qualitative semi-structured interviews with participants representing these case examples. Qualitative content analysis was used to synthesize all data into key themes and recommendations for practice. Results: Our search found 52 peer-reviewed articles; we interviewed participants from 2 case examples. There was no consistent definition or framework of teams/teamwork, success, or interprofessional MoC in the literature findings nor our interview findings. Despite this, common themes emerged across both literature and interview findings. These themes were synthesized into the following: Interprofessionally-oriented leadership, clear roles and expectations, teamwork, collaboration, trust integrity and transparency, valuing diverse perspectives, growth mindset, and the criticality of context. Drawing on the thematic synthesis of our findings, we developed 4 core recommendations: (). Design teams with a few core or anchor roles and multiple floating or flexible roles (2) Implement routine team processes (e.g huddles)or a similar space and channel for open communication, (3) re-envision and Repurpose staff to combat resource scarcity, (4) Recognize that addressing HHR gaps or barriers requires tailored and targeted interventions.Implications: This project yielded three broad opportunities for provincial and territorial consideration to enable the implementation of innovative and impactful models of care in hospitals: bolstering education and training, enhancing information and evaluation sharing, and breaking down barriers while building up incentives. Our data made it evident that there is no single model of care suited for implementation on a pan-Canadian scale. However, tailored interventions leveraging the existing needs, resources, and contexts of different hospital settings can be effective in improving team cohesion, staff retention, and patient and provider experience of care.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".