Analysis of case management programs for patients with dementia: A systematic review
Bibliographic record
Abstract
BACKGROUND: People suffering from dementia are particularly vulnerable to the gaps between the health and social service systems. Case management is a professional field that seeks to fill in these gaps and remedy this fragmentation. METHODS: We report the results of a systematic literature review of the impact of case management programs on clinical outcomes and the utilization of resources by persons with dementia. We focused on randomized controlled trials (RCTs) and attempted to identify the factors that might contribute to greater program efficacy. Because the evaluation methods in these studies varied, we used the effect size method to estimate the magnitude of the statistically significant effects reported. RESULTS: Our search strategy identified 17 references relating to six RCTs. Four of these six RCTs reported moderately statistically significant effects (effect size, 0.2-0.8) on their primary end point: the clinical outcome in three and resource utilization in one. Two of the RCTs reported weak or no effects (effect size, <0.2) on their primary end point. Because of the wide variety of the end points used, an overall effect size could not be calculated. Parameters that appear to be related to greater case management efficacy are the integration level between the health and social service organizations and the intensity of the case management. CONCLUSIONS: Integration and case management intensity seem to determine the magnitude of the clinical effects in this new professional field. Further studies are needed to clarify the economic impact.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".