Extending Integration: Interventions Supporting Communication and Collaboration Between Patients with Neurological Diseases, Their Informal Caregivers and Healthcare Staff – a Scoping Review
Bibliographic record
Abstract
Introduction: Addressing challenges due to demographic changes and the quest for improved value in healthcare requires an extended integrated approach to care that fosters collaboration between all stakeholders, especially within collaboration supporting cognitively impaired patients. The aim is to review existing studies on interventions to improve communication and collaboration between such patients, their caregivers and healthcare staff. Methods: Following PRISMA guidelines, we systematically searched electronic databases Medline (OVID), CINAHL (Ebsco), and Web of Science (Clarivate) for peer-reviewed literature [2010–2020] focusing on intervention studies. Papers were excluded if not assessing the impact of interventions or only presenting a study protocol. Results: Twelve studies explored diverse approaches to social support, all with the aim of improving communication and collaboration among stakeholders, and identified three intervention types: supporting empowerment, promoting collaborative disease management, and coping, and enhancing communication and relationships. Discussion: The interventions employed various approaches and assessed a range of outcomes, demonstrating the benefits of enhancing communication and collaboration among stakeholders. Yet only a few studies included the full triad of partners in care. Conclusion: There is still much to be done to achieve the extended integration of care services and support that will benefit from patient and caregiver involvement.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".