Abstract WP389: Understanding the Clinicians’ Experiences in Collecting Stroke Rehabilitation Intensity Data Within Ontario
Bibliographic record
Abstract
Background and Issues: The collection and reporting of Rehabilitation Intensity (RI) in a national rehabilitation database was mandated on April 1, 2015 for all stroke patients within Ontario, to support evaluation of stroke best practice implementation. RI includes minutes of direct task-specific therapy patients experience per day. This requires a shift in thinking from capturing the clinician’s time spent in therapy to the patient perspective. To ensure that high quality data is collected, it was important to understand clinicians’ experiences in collecting RI data. Purpose: To identify enablers and barriers to RI data collection in order to inform the development of resources to support clinicians. Methods: A 12-item electronic survey was developed by an Ontario Stroke Network (OSN) task group to evaluate the clinician experience of RI data collection (including: demographics, barriers, enablers, education, resources, and practice change). The survey was distributed via SurveyMonkey® and sent to clinicians from 48 hospitals, 3 weeks post implementation of RI data collection. Analyses involved descriptive statistics and thematic analysis. Results: Three hundred and twenty-one clinicians from 47 hospitals responded to the survey. Survey results suggest RI data collection is feasible; seventy-one percent of clinicians report it takes 10 minutes or less to enter RI data. Thematic analysis identified: 5 common challenges with most frequently reported relating to data quality, 30% (N=358) and 6 common enablers with most frequently reported relating to ease of collecting RI data through workload measurement systems, 50% (N=46). Suggestions for educational resources included tools for identifying what is included in RI and the provision of education (e.g. webinars). Conclusions: RI data collection is feasible for clinicians. Education and resources developed should support key challenges and enablers identified by clinicians - to enhance data quality and the consistency of RI collection. As RI data fields are available through a national rehabilitation database, this work sets the foundation for other provinces interested in the systematic collection and reporting of RI data.
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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 | 0.025 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".