Implementing integrated care pilot projects in hospital settings – an exploration of disruptive practices
Bibliographic record
Abstract
Purpose In Canada, integrated care pilot projects are often implemented as a local reform strategy to improve the quality of patient care and system efficiencies. In the qualitative study reported here, the authors explored the experiences of healthcare professionals when first implementing integrated care pilot projects, bringing together physical and mental health services, in a community hospital setting. Design/methodology/approach Engaging a qualitative descriptive study design, semi-structured interviews were conducted with 24 healthcare professionals who discussed their experiences with implementing three integrated care pilot projects one year following project launch. The thematic analysis captured early implementation issues and was informed by an institutional logics framework. Findings Three themes highlight disruptions to established logics reported by healthcare professionals during the early implementation phase: (1) integrated care practices increased workload and impacted clinical workflows; (2) integrating mental and physical health services altered patient and healthcare provider relationships; and (3) the introduction of integrated care practices disrupted healthcare team relations. Originality/value Study findings highlight the importance of considering existing logics in healthcare settings when planning integrated care initiatives. While integrated care pilot projects can contribute to organizational, team and individual practice changes, the priorities of healthcare stakeholders, relational work required and limited project resources can create significant implementation barriers.
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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 | 0.048 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| 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".