Dementia care in the context of primary care reform: an integrative review
Bibliographic record
Abstract
OBJECTIVES: The purpose of this review was to determine the influence of the growing body of evidence about the attributes of high-performing primary care systems on the literature related to the primary care of people living with dementia. METHODS: In this integrative review, we examined a broad range of published and grey literature (2000-2013) about the primary care of dementia, using a systematic approach set up in advance of the literature search. The review was guided by two questions: What are the evident models of primary care for dementia? How do the models line up with the other attributes of high-performing primary care? RESULTS: Three models were noted: carved-out, co-managed, and integrative-hub, all informed by different assumptions about the role of primary care in dementia. The models varied in alignment with the attributes of high-performing primary care, although we found very little attention to accessibility, relational continuity or comprehensiveness of care. CONCLUSIONS: We know what we need to pay attention to in building our primary care system - and no population will put the performance of primary care more to the test over the next two decades than the rapidly growing number of people who will be living with complex chronic conditions like dementia. Recent literature around primary care and dementia shows promise in attending to some of the attributes of high-performing primary care, yet much more work is needed if we are to truly leverage the potential value of primary care in addressing the needs of these complex and numerous future patients.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".