Methods for development of structure, process and outcome indicators for prioritized spinal cord injury rehabilitation Domains: SCI-High Project
Bibliographic record
Abstract
Background: High-quality rehabilitation care following spinal cord injury or disease (SCI/D) is critical for optimizing neurorecovery and long-term health outcomes. This manuscript describes the methods used for developing, refining, and implementing a framework of structure, process, and outcome indicators that reflect high-quality rehabilitation among adults with SCI/D in Canada.Methods: This quality improvement initiative was comprised of the following processes: (1) prioritization of care Domains by key stakeholders (scientists, clinicians, therapists, patients and stakeholder organizations); (2) assembly of 11 Domain-specific Working Groups including 69 content experts; (3) conduct of literature searches, guideline and best practice reviews, and outcome synthesis by the Project Team; (4) refinement of Domain aim and construct definitions; (5) conduct of cause and effect analysis using Driver diagrams; (6) selection and development of structure, process and outcome indicators; (7) piloting and feasibility analysis of indicators and associated evaluation tools; and, (8) dissemination of the proposed indicators.Result: The Project Team established aims, constructs and related structure, process, and outcome indicators to facilitate uniform measurement and benchmarking across 11 Domains of rehabilitation, at admission and for 18 months thereafter, among adult Canadians by 2020.Conclusion: These processes led to the selection of a feasible set of indicators that once implemented should ensure that adults with SCI/D receive timely, safe, and effective rehabilitation services. These indicators can be used to assess health system performance, monitor the quality of care within and across rehabilitation settings, and evaluate the rehabilitation outcomes of the population to ultimately enhance healthcare quality and equity.
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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 | 0.095 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".