Use of Motivational Interviewing in Older Patients with Multiple Chronic Conditions and Their Informal Caregivers: A Scoping Review
Bibliographic record
Abstract
The use of motivational interviewing is relatively new in multiple chronic conditions (MCCs). A scoping review was conducted according to JBI methodology to identify, map and synthesize existing evidence on the use of motivational interviewing to support self-care behavior changes in older patients with MCCs and to support their informal caregivers in promoting patient self-care changes. Seven databases were searched, from database inception to July 2022, for studies that used motivational interviewing in interventions for older patients with MCCs and their informal caregivers. We identified 12 studies, reported in 15 articles, using qualitative, quantitative, or mixed-method designs, conducted between 2012 and 2022, describing the use of motivational interviewing for patients with MCCs. We could not locate any study regarding its application for informal caregivers. The scoping review showed that the use of motivational interviewing is still limited in MCCs. It was used mainly to improve patient medication adherence. The studies provided scant information about how the method was applied. Future studies should provide more information about the application of motivational interviewing and should address self-care behavior changes relevant to patients and healthcare providers. Informal caregivers should also be targeted in motivational interviewing interventions, as they are essential for the care of older patients with MCCs.
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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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".