Improving practice through collaboration: Early experiences from the multi-site Spinal Cord Injury Implementation and Evaluation Quality Care Consortium
Bibliographic record
Abstract
CONTEXT: Dedicated implementation efforts are critical to bridging the gaps between current practices and best practices. A quality improvement collaborative (QIC), the Spinal Cord Injury Implementation and Evaluation Quality Care Consortium (SCI IEQCC), was established to meet this need, bringing together a network of clinicians and administrators to systematically improve the quality and equity of tertiary spinal cord injury or disease (SCI/D) rehabilitation care in Ontario, Canada. METHODS: Clinicians and leaders from five tertiary SCI/D rehabilitation centers and two not-for-profit SCI/D advocacy groups comprised a network dedicated to supporting implementation of the SCI-High quality indicators in prioritized domains of SCI rehabilitation and related best practices by: (1) building capacity through implementation science education of frontline clinicians; (2) providing resources and support to empower frontline clinicians to lead quality improvement efforts within their institutions; (3) promoting wider learning through a network for sharing ideas, efforts, and experiences; and (4) collecting indicator data to facilitate provincial evaluation of goal attainment. RESULTS: Network members and sites collaborated to implement best practices within six priority domains; in 18 months, significant progress has been made in emotional wellbeing, sexual health, walking, and wheeled mobility despite disruptions due to the COVID-19 pandemic. These efforts encompass heterogeneous challenges and strategies, ranging from developing clinical skills programs, to streamlining processes, to manipulating physical space. CONCLUSION: A QIC targeting SCI/D rehabilitation demonstrates promise for advancing the implementation of best practices, building implementation science capacity across multiple sites, and for promoting collaboration amongst SCI/D rehabilitation centers and organizational partners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".