SUSTAINABILITY AND SPREAD OF MOVE ON: A MOBILIZATION INITIATIVE TWO YEARS AFTER IMPLEMENTATION
Bibliographic record
Abstract
Mobilization of older patients in hospitals is a clinical care priority that can reduce functional decline, delirium and length of stay. The Mobilization of Vulnerable Elders in Ontario (MOVE ON) initiative promoted 3 core messages: Mobilization should occur at least 3 times daily, mobilization should be progressive and scaled, assessment should take place within 24 hours of admission. Coordinated centrally, MOVE ON included 14,540 patients in 14 hospitals, mean age 79.9 years. In interrupted time series analysis, 10.56% more patients mobilized compared to pre-intervention. Using mixed methods, including 212 staff surveys, we identified success factors for spread and sustainability. Success factors for sustainability included contextualized education, cultural shift, implementation of formal procedures (policies, role revision, and documentation), visible corporate support, collaborative resource sharing and alignment with system priorities. The spread of MOVE ON continues, being adapted in over 40 hospitals in Ontario, and to other provinces and countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".