Case Management in Primary Care for Frequent Users of Health Care Services: A Mixed Methods Study
Bibliographic record
Abstract
PURPOSE: This study aimed to evaluate the effects of the V1SAGES case management intervention (Vulnerable Patients in Primary Care: Nurse Case Management and Self-management Support) for frequent users of health care services with chronic disease and complex care needs on psychological distress and patient activation. METHODS: We used a 2-phase sequential mixed methods design. The first phase was a pragmatic randomized controlled trial with intention-to-treat analysis that measured the effects of the intervention compared with usual care on psychological distress and patient activation before and after 6 months. The second phase had a qualitative descriptive design and entailed thematic analysis of in-depth interviews (25 patients, 6 case management nurses, 9 health managers) and focus groups (8 patients' spouses, 21 family physicians) to understand stakeholders' perceived effects of the intervention on patients. RESULTS: = .43). Qualitative results suggested that patients and their spouses benefitted from the case management intervention, gaining a sense of security, and stakeholders noted better patient self-management of health. CONCLUSIONS: Together, our study's quantitative and qualitative results suggest that case management reduces psychological distress, making patients and caregivers feel more secure, whereas impact on self-management is unclear. Case management is a promising avenue to improve outcomes among frequent users of health care with complex needs.
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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 | 0.032 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".