Mapping the evidence on dementia care pathways – A scoping review
Bibliographic record
Abstract
BACKGROUND: One way of standardizing practice and improving patient safety is by introducing clinical care pathways; however, such pathways are typically geared towards assisting clinicians and healthcare organizations with evidence-based practice. Many dementia care pathways exist with no agreed-upon version of a care pathway and with little data on experiences about their use or outcomes. The objectives of the review were: (1) to identify the dementia care pathway's purpose, methods used to deploy the pathway, and expected user types; (2) to identify the care pathway's core components, expected outcomes, and implications for persons with dementia and their care partners; and (3) determine the extent of involvement by persons with dementia and/or their care partners in developing, implementing, and evaluating the care pathways. METHODS: We systematically searched six literature databases for published literature in the English language in September 2023 utilizing Arskey and O'Malley's scoping review framework. RESULTS: The findings from the dementia care pathways (n = 13) demonstrated assistance in dementia diagnostic and management practices for clinicians and offered structured care processes in clinical settings. For this reason, these pathways emphasized assessment and interventional post-diagnostic support, with less emphasis on community-based integrated dementia care. CONCLUSION: Future dementia care pathway development can seek the involvement of persons with dementia and care partners in designing, implementing and evaluating such pathways, ensuring that outcome measures properly reflect the impact on persons with lived dementia experience and their care partners.
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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 | 0.045 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.039 | 0.037 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".