Impact of the End PJ Paralysis interventions on patient health outcomes at the participating hospitals in Alberta, Canada
Bibliographic record
Abstract
Purpose Multiple hospitals in Alberta implemented the End PJ Paralysis – a multicomponent inpatient ambulation initiative aimed at preventing the adverse physical and psychological effects patients experience due to low mobility during admission. To inform a scale-up strategy, this study assessed the impact of the initiative based on select process and outcome measures.Materials and methods Clinical and administrative data were obtained from the hospital Discharge Abstract Database, Research Electronic Data Capture (Redcaps), and Reporting and Learning System for Patient Safety. The variables explored were length of stay, inpatient falls, discharge disposition, pressure injury, patient ambulation, and patient dressed rates. We then used the Interrupted Time Series design for impact analysis.Results The analysis included discharge abstracts for 32,884 patients and the results showed significant improvements in outcomes at the participating units. The length of stay and inpatient falls were reduced immediately by 1.8 days (B2=-1.80, p = 0.044, 95% CI [-3.54, −0.05]), and 2.2 events (B2=-2.22, p = 005, 95% CI [-3.75, −0.69]). The percentage of patients discharged home increased overtime (B2=.39, p=.006, 95% CI [.11, .66]). Mobilization and dressed rates also improved.Conclusions The findings imply the interventions safely mitigated the risk of immobility-induced complications, including deconditioning and hospital-acquired disability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".