Poster (Health Services, Economics and Policy Change) ID 1985397
Bibliographic record
Abstract
Background The Spinal Cord Injury Implementation and Evaluation Quality Care Consortium (SCI-IEQCC) is a quality improvement initiative (began in 2019) with a mission to provide optimal and equitable rehabilitation services for all Canadians regardless of where they live, and to ensure the functional recovery, health and well-being for individuals living with spinal cord injury and disease (SCI/D). Objective To highlight the growing needs of an expanding multisite network and the importance of governance and organizational structures to support the execution of current deliverables while addressing emerging challenges. Design/Methods The SCI-IEQCC has a central team for network management. Administrators/leaders, clinicians with implementation science training, local site implementation teams, and individuals with lived experience comprise the SCI-IEQCC. Implementation and evaluation of indicators and best practices across 10 participating organizations requires: operational oversite; monitoring of project milestones; adherence to regulatory requirements; sustained data quality with linkage for analyses; report card dissemination; information sharing and network updates; strategic planning to map future directions; identification of priorities; and, meeting academic/funding deliverables. Results/Findings SCI-IEQCC has driven improvements in rehabilitation service delivery, performance, utilization of best practices, and increments in staff engagement, training, and knowledge exchange. SCI-IEQCC challenges are derived from the diversity of participating organizations, turnover in leaders, staff shortages, reducing variation in documentation and data management, and ensuring resource continuity. Conclusion The growing complexity of this multisite network necessitate innovative approaches to streamline processes across sites, ensure uniform adherence to regulatory requirements, disseminate innovations in informatics, and assure dedication to its mission and vision.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.909 | 0.651 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".