Family Physician–Case Manager Collaboration and Needs of Patients With Dementia and Their Caregivers: A Systematic Mixed Studies Review
Bibliographic record
Abstract
PURPOSE: Dementia case management (CM) in primary care is a complex intervention aimed at identifying the various needs of patients with dementia and their caregivers, as well as the organization and coordination of care. A key element of CM is the collaboration of family physicians with case managers. We conducted a systematic mixed-studies review to identify the needs of the patient-caregiver dyad and the effects of CM. METHODS: We searched MEDLINE, PsycINFO, and EMBASE up to October 2014, regardless of the study design. Our main outcomes were needs of patients and their caregivers and the effects of CM on these needs. We used narrative syntheses to develop a taxonomy of needs and to describe the effects of CM on those needs. We used meta-analysis to calculate the prevalence of needs and the standardized mean differences to evaluate the effects of CM on the needs identified. RESULTS: Fifty-four studies were included. We identified needs of the patient-caregiver dyad and needs of the patient and caregiver individually. CM addressed the majority of the identified needs. Still, some very common needs (eg, early diagnosis) are overlooked while other needs (eg, education on the disease) are well addressed. Fully establishing the value of CM is difficult given the small number of studies of CM in primary care. CONCLUSIONS: There is good evidence that case managers, in collaboration with family physicians, have a pivotal role in addressing the needs of the patient-caregiver dyad.
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".