Better care for people with complex care needs: a partnership between primary care clinics and the community network
Bibliographic record
Abstract
Context: Patients with complex health and social care needs face significant service coordination and integration issues. They often require a variety of services from different programs and the community network. A case management program (CMP) for patients with complex care needs was implemented in primary care clinics to improve services coordination with community resources and integrated health and social services centers. Objective: Identify factors facilitating or hindering: 1) the implementation of the CMP in primary care clinics; and 2) interactions with community resources. Study Design and Analysis: A qualitative descriptive multiple case study using an inductive thematic analysis approach. Setting or Dataset: Four clinics in an urban area of the province of Quebec (Canada), where the CMP was implemented. Population Studied: Key informants (n=36) involved in the implementation of the CMP: 2 implementation project managers, 4 clinic managers, 1 case manager from an integrated health and social services center, 8 case managers in primary care clinics, 4 physician leads, and 5 other healthcare professionals. Method: Semi-structured interviews and focus groups with key informants, and participant observation (n=12 hours) during executive meetings. Outcome Measures: Themes that emerged from the inductive thematic analysis. Results: The active support of an experienced case manager from the integrated health and social center, the participation in a community of practice, and the collaboration between social workers and nurses, helped case managers to engage, gain confidence in performing new tasks and be aware of community resources. The density of services in the urban area presented advantages in terms of various services adapted to the patient’s needs but also raised care coordination challenges for patients who use multiple services over a wide area. More interaction between internal and external partners would have been useful in monitoring the implementation process, particularly with family physicians. Limited access to mental care services may hinder the engagement of case managers, especially with patients with important mental health challenges. Conclusions: This study informs policy makers, clinicians, and researchers on levers and pitfalls to avoid in the implementation of complex interventions such as CMP for patients with complex care needs in primary care settings.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".