Case management for dementia in primary health care: a systematic mixed studies review based on the diffusion of innovation model
Bibliographic record
Abstract
Vladimir Khanassov, Isabelle Vedel, Pierre PluyeDepartment of Family Medicine, McGill University, Montreal, QC, CanadaBackground: The purpose of this study was to examine factors associated with the implementation of case management (CM) interventions in primary health care (PHC) and to develop strategies to enhance its adoption by PHC practices.Methods: This study was designed as a systematic mixed studies review (including quantitative and qualitative studies) with synthesis based on the diffusion of innovation model. A literature search was performed using MEDLINE, PsycInfo, EMBASE, and the Cochrane Database (1995 to August 2012) to identify quantitative (randomized controlled and nonrandomized) and qualitative studies describing the conditions limiting and facilitating successful CM implementation in PHC. The methodological quality of each included study was assessed using the validated Mixed Methods Appraisal Tool. Results: Twenty-three studies (eleven quantitative and 12 qualitative) were included. The characteristics of CM that negatively influence implementation are low CM intensity (eg, infrequent follow-up), large caseload (more than 60 patients per full-time case manager), and approach, ie, reactive rather than proactive. Case managers need specific skills to perform their role (eg, good communication skills) and their responsibilities in PHC need to be clearly delineated.Conclusion: Our systematic review supports a better understanding of factors that can explain inconsistent evidence with regard to the outcomes of dementia CM in PHC. Lastly, strategies are proposed to enhance implementation of dementia CM in PHC. Keywords: systematic mixed studies review, dementia, case management, primary health care, implementation, diffusion of innovation
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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 | 0.069 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".