MOVE (MOBILIZATION OF VULNERABLE ELDERS) AB INITIATIVE FOR INPATIENTS IN ALBERTA COMMUNITY HOSPITALS
Bibliographic record
Abstract
The objective of MOVE AB was to disseminate, implement and evaluate in community hospitals in Alberta, Canada an evidence based strategy that had been successful in promoting early mobilization in older patients admitted to academic hospitals in Ontario, Canada. Early mobilization strategies have been shown to improve both patient and system outcomes. Four community-based hospitals in Alberta participated. The multi-disciplinary approach focused on three key messages: 1. encourage mobility three times a day, 2. progressive and scaled mobilization, and 3. mobility assessments should be implemented within 24 hours of admission. MOVE AB was delivered in phases: Planning/pre-intervention, Intervention and Post-intervention. Key planning activities included a Readiness assessment and Barriers and Facilitators survey, which allowed for tailored interventions to each unit participating. Interventions included coaching, fairs, huddles, educational materials, e-modules as well as focusing on natural opportunities. The primary outcome was the proportion of patients aged 65 and older who were mobilized during their hospital stay. Audits were conducted though all study phases, twice a week, 3 times a day. Average mobilization rates increased over time (pre-intervention= 42.5%, intervention= 43.4%, post-intervention= 45.6%). Average mobility rates were highest during lunch (57.4%) and increased by 8% from pre-intervention to post-intervention. The majority of mobile activity consisted of sitting in a chair; sitting in bed with legs dangling or standing/walking in room independently. Additional analyses will include examining impact on length of stay and discharge location. However, we were able to successfully disseminate the MOVE initiative from academic hospitals into smaller community hospitals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".