Conception and development of Self-Management indicators to advance the quality of spinal cord injury rehabilitation: SCI-High Project
Bibliographic record
Abstract
Context Although self-management is linked to reduced secondary health complications (SHCs) and enhanced overall quality of life post-spinal cord injury or disease (SCI/D), it is poorly integrated into the current rehabilitation process. Promoting self-management and assuring equity in care delivery is critical. Herein, we describe the selection of Self-Management structure, process and outcome indicators for adults with SCI/D in the first 18 months after rehabilitation admission.Methods Experts in self-management across Canada completed the following tasks: (1) defined the Self-Management construct; (2) conducted a systematic search of available outcomes and their psychometric properties; and (3) created a Driver diagram summarizing available evidence related to Self-Management. Facilitated meetings allowed development and selection following rapid-cycle evaluations of proposed structure, process and outcome indicators.Results The structure indicator is the proportion of staff with appropriate education and training in self-management principles. The process indicator is the proportion of SCI/D inpatients who have received a self-management assessment related to specific patient self-management goal(s) within 30 days of admission. The outcome indicator is the Skill and Technique Acquisition, and Self-Monitoring and Insight subscores of the modified Health Education Impact Questionnaire.Conclusion The structure indicator will heighten awareness among administrators and policy makers regarding the need to provide staff with ongoing training related to promoting self-management skill acquisition. Successful implementation of the Self-Management process and outcome indicators will promote self-management education and skill acquisition as a rehabilitation priority, allow for personalization of skills related to the individual’s self-management goal(s), and empower individuals with SCI/D to manage their health and daily activities while successfully integrating into the community.
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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 | 0.040 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".