Sustaining Stroke Rehabilitation intensity : Evaluating Clinician Knowledge Across Ontario
Bibliographic record
Abstract
Background:Intensive rehabilitation is important to optimize recovery in inpatient stroke rehabilitation. In Ontario Canada, rehabilitation intensity (RI) includes active goal directed one-to-one therapy, which is monitored or guided by a therapist. Stroke RI is measured in minutes and collected, monitored and publicly reported. Through stakeholder engagement, a need was identified to develop a provincial resource to support ongoing RI education. Methods:A standard learning module was developed to provide RI education and evaluate clinician knowledge across the province. The draft module was piloted, refined and validated by clinicians and other stakeholders. The final module included an educational slide deck, 12-item quiz and feedback form. The dissemination process began in December 2017 through an existing infrastructure, with dissemination methods varying by region. Quiz results and feedback informed continuous improvement of the module. Results:Within the first three months, 115 clinicians from 18 organizations completed this module. Scores ranged from 62-100% (median: 92%; mean: 91%). Preliminary data indicate that most errors occurred with questions related to recording of therapy assistant time or collaborative treatment. More organizations and clinicians are expected to complete the module as it is implemented across the province. Initial feedback did not necessitate extensive changes to module format or content. Conclusion: Although data are still being collected, preliminary findings suggest a need for further education related to the measurement of therapy assistant time and collaborative treatment. To support sustainability of RI, results from this work will inform the ongoing development of new RI education or knowledge translation tools.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".