The Effect of Caregiver-Mediated Mobility Interventions in Hospitalized Patients on Patient, Caregiver, and Health System Outcomes: A Systematic Review
Bibliographic record
Abstract
To synthesize the evidence examining caregiver-mediated mobility interventions in a hospital setting and whether they improve patient, caregiver, or health system outcomes. We searched MEDLINE, EMBASE, PsycINFO, CINAHL, and Scopus databases from inception to September 7, 2018. Two reviewers independently selected original research in inpatient settings that reported on an intervention delivered by a caregiver (eg, family, friend, paid worker) and directed to the patient’s mobility. Mobility interventions were categorized based on the level of caregiver engagement using a 3-category framework: inform (provision of education on patient’s condition and management), activate (prompting caregivers to take action in patient care), and collaborate (encouraging interaction with providers or other caregivers). One reviewer extracted data, and another checked the data. Quality was assessed using the Cochrane Collaboration’s risk of bias tool and Grading of Recommendations, Assessment, Development and Evaluation approach. Forty studies met the inclusion criteria; most were randomized controlled trials (n=16/40, 40.0%) and investigated older adults (n=18/40, 45.0%) with stroke (n=20/40, 50.0%). Inform (n=2) and activate (n=4) interventions and combined inform-activate (n=5/6, 83.3%) and inform-activate-collaborate (n=6/10, 60.0%) interventions were reported to improve patient mobility. Inform-activate and inform-collaborate interventions were reported to improve caregiver outcomes (eg, burden) (n=13/19, 68.4%). Studies that engaged caregivers in all 3 strategies (inform-activate-collaborate) were reported to improve health system outcomes (eg, hospital readmission) (n=4/6, 66.7%). Most studies were of unclear (n=22/40, 55.0%) or low risk of bias (n=11/40, 27.5%) for most domains. Engaging caregivers in mobility of hospitalized patients may improve patient mobility as well as caregiver and health system outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".