ENGAGING THE HEALTH CARE TEAM TO PROMOTE PATIENT ACTIVATION FOR ENHANCED STROKE RECOVERY
Bibliographic record
Abstract
Activating stroke survivors in acute care is challenging. Engaging the care team to meet the challenge is complex. Canadian Stroke Best Practices advocate for early patient activation to promote recovery. Our team is currently implementing a multi-pronged strategy to promote patient activation for stroke patients during their hospital stay. Objectives: 1) Assess the effect of a music-enhanced movement program on a) amount of time patients are active during their hospital stay, b) length of stay and c) satisfaction with care received. 2) Determine to what extent a series of education capsules for patient care attendants (PCA) will improve knowledge, enhance skills and change practice behaviors related to patient activation on an acute stroke unit. Methods: The music-enhanced program is currently delivered by student volunteers, with supervision from a recreational therapist. Volunteers track patient-related outcomes. Program-related outcomes such as length of stay and satisfaction with care will be analysed pre/post two iterations. The education capsules will be evaluated using a multiple time series design. Results: A first iteration of the music-enhanced movement program resulted in 109 visits by student volunteers and in 21 hours of additional activation for patients. The effect on length of stay and satisfaction of care are currently being examined. The education capsules for the patient care attendants are well-received to date. Conclusions: Patient-related and organizational factors need to be considered when developing innovative approaches in order to enhance their probability of sustainability. Patient activation can begin early after stroke, in acute care, with results that impact care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".