Prioritization of rehabilitation Domains for establishing spinal cord injury high performance indicators using a modification of the Hanlon method: SCI-High Project
Bibliographic record
Abstract
Objectives: To prioritize Domains of SCI Rehabilitation Care (SCI-Care) based on clinical importance and feasibility to inform the development of indicators of quality SCI-Care for adults with SCI/D in Canada. Methods: A 17-member external advisory committee, comprised of key stakeholders, ranked 15/37 Domains of rehabilitation previously flagged by the E-scan project team for gaps between knowledge generation and clinical implementation. Priority scores (D) were calculated using the Hanlon formula: D=[A+(2×B)]×C, where A is prevalence, B is seriousness, and C is the effectiveness of available interventions. A modified “EAARS” (Economic, Acceptability, Accessibility, Resources, and Simplicity) criterion was used to rank feasibility on a scale of 0–4 (4 is high). The product of these two scores determined the initial Domain ranking. Following the consensus process, further changes were made to the Domain rankings. Results: Despite a low feasibility score, Sexual Health was ranked as high priority; and, the Community Participation and Employment Domains were merged. The 11 final prioritized Domains in alphabetic order were: Cardiometabolic Health; Community Participation and Employment; Emotional Well-Being; Reaching, Grasping, and Manipulation; Self-Management; Sexual Health; Tissue Integrity; Urinary Tract Infection; Urohealth; Walking, and Wheeled Mobility. Conclusions: The modified Hanlon method was used to facilitate prioritization of 11 of 37 Domains to advance the quality of SCI-care by 2020. In future, the Spinal Cord Injury Rehabilitation Care High Performance Indicators (SCI-High) Project Team will develop structure, process and outcome indicators for each prioritized Domain.
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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 | 0.059 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".