Case management in primary care for frequent users of healthcare services with chronic diseases and complex care needs: an implementation and realist evaluation protocol
Bibliographic record
Abstract
INTRODUCTION: Significant evidence in the literature supports case management (CM) as an effective intervention to improve care for patients with complex healthcare needs. However, there is still little evidence about the facilitators and barriers to CM implementation in primary care setting. The three specific objectives of this study are to: (1) identify the facilitators and barriers of CM implementation in primary care clinics across Canada; (2) explain and understand the relationships between the actors, contextual factors, mechanisms and outcomes of the CM intervention; (3) identify the next steps towards CM spread in primary care across Canada. METHODS AND ANALYSIS: We will conduct a multiple-case embedded mixed methods study. CM will be implemented in 10 primary care clinics in five Canadian provinces. Three different units of analysis will be embedded to obtain an in-depth understanding of each case: the healthcare system (macro level), the CM intervention in the clinics (meso level) and the individual/patient (micro level). For each objective, the following strategy will be performed: (1) an implementation analysis, (2) a realist evaluation and (3) consensus building among stakeholders using the Technique for Research of Information by Animation of a Group of Experts method. ETHICS AND DISSEMINATION: This study, which received ethics approval, will provide innovative knowledge about facilitators and barriers to implementation of CM in different primary care jurisdictions and will explain how and why different mechanisms operate in different contexts to generate different outcomes among frequent users. Consensual and prioritised statements about next steps for spread of CM in primary care from the perspectives of all stakeholders will be provided. Our results will offer context-sensitive explanations that can better inform local practices and policies and contribute to improve the health of patients with complex healthcare needs who frequently use healthcare services. Ultimately, this will increase the performance of healthcare systems and specifically mitigate ineffective use and costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".