Protocol for a mixed-method analysis of implementation of case management in primary care for frequent users of healthcare services with chronic diseases and complex care needs
Bibliographic record
Abstract
INTRODUCTION: Case management (CM) in a primary care setting is a promising approach to integrating and improving healthcare services and outcomes for patients with chronic conditions and complex care needs who frequently use healthcare services. Despite evidence supporting CM and interest in implementing it in Canada, little is known about how to do this. This research aims to identify the barriers and facilitators to the implementation of a CM intervention in different primary care contexts (objective 1) and to explain the influence of the clinical context on the degree of implementation (objective 2) and on the outcomes of the intervention (objective 3). METHODS AND ANALYSIS: A multiple-case embedded mixed-methods study will be conducted on CM implemented in ten primary care clinics across five Canadian provinces. Each clinic will represent a subunit of analysis, detailed through a case history. Cases will be compared and contrasted using multiple analytical approaches. Qualitative data (objectives 1 and 2) from individual semistructured interviews (n=130), focus group discussions (n=20) and participant observation of each clinic (36 hours) will be compared and integrated with quantitative (objective 3) clinical data on services use (n=300) and patient questionnaires (n=300). An evaluation of intervention fidelity will be integrated into the data analysis. ETHICS AND DISSEMINATION: This project received approval from the CIUSSS de l'Estrie - CHUS Research Ethic Board (project number MP-31-2019-2830). Results will provide the opportunity to refine the CM intervention and to facilitate effective evaluation, replication and scale-up. This research provides knowledge on how to resp ond to the needs of individuals with chronic conditions and complex care needs in a cost-effective way that improves patient-reported outcomes and healthcare use, while ensuring care team well-being. Dissemination of results is planned and executed based on the needs of various stakeholders involved in the research.
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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 | 0.167 | 0.140 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.115 | 0.024 |
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".