Case Management in Primary Care for Frequent Users of Health Care Services: A Realist Synthesis
Bibliographic record
Abstract
PURPOSE: Case management (CM) is a promising intervention for frequent users of health care services. Our research question was how and under what circumstances does CM in primary care work to improve outcomes among frequent users with chronic conditions? METHODS: We conducted a realist synthesis, searching MEDLINE, CINAHL, Embase, and PsycINFO (1996 to September 2017) for articles meeting the following criteria: (1) population: adult frequent users with chronic disease, (2) intervention: CM in a primary care setting with a postintervention evaluation, and (3) primary outcomes: integration of services, health care system use, cost, and patient outcome measures. Academic and gray literature were evaluated for relevance and robustness. Independent reviewers extracted data to identify context, mechanism, and outcome (CMO) configurations. Analysis of CMO configurations allowed for the modification of an initial program theory toward a refined program theory. RESULTS: Of the 9,295 records retrieved, 21 peer-reviewed articles and an additional 89 documents were retained. We evaluated 19 CM interventions and identified 11 CMO configurations. The development of a trusting relationship fostering patient and clinician engagement in the CM intervention was recurrent in many CMO configurations. CONCLUSION: Our refined program theory proposes that in the context of easy access to an experienced and trusted case manager who provides comprehensive care while maintaining positive interactions with patients, the development of this relationship fosters the engagement of both individuals and yields positive outcomes when the following mechanisms are triggered: patients and clinicians feel supported, respected, accepted, engaged, and committed; and patients feel less anxious, more secure, and empowered to self-manage.
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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 | 0.078 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".