Using scoping review methods to describe current capacity and prescribe change in Canadian SCI rehabilitation service delivery
Bibliographic record
Abstract
OBJECTIVE: To describe the methodology used to conduct a scoping review of spinal cord injury (SCI) rehabilitation service delivery in Canada, and to explain the reporting process intended to advance future service delivery. EVIDENCE ACQUISITION: A SCI rehabilitation framework derived from the International Classification of Function, Disability and Health was developed to describe the goals and interprofessional processes of rehabilitation. An adapted Arksey and O'Malley (2005) methodological framework was used to conduct a scoping review of SCI rehabilitation services in Canada. Data were obtained from multiple relevant sources via survey (N = 3572 data fields) from 13 of 15 Canadian tertiary SCI rehabilitation sites, systematic reviews, white papers, literature reviews, clinical practice resources, and clinicians. Multidisciplinary teams of content experts (N = 17), assisted with data interpretation and validation by articulating practice trends, gaps, and priorities. EVIDENCE SYNTHESIS: The findings will be presented in an atlas, which includes aggregate national data regarding impairment and demographic characteristics, service utilization, available resources (staff and capital equipment), specialized services, local expertise, and current best practice indicators, outcome measures, and clinical guidelines. Data were collated and synthesized relative to specific rehabilitation goals. The current state of SCI rehabilitation service delivery (specific to each rehabilitation goal) is summarized in a report card within three domains, knowledge generation, clinical application, and policy change, and specifies key 2020 priorities. CONCLUSION: These findings should prompt critical evaluation of current Canadian SCI rehabilitation service delivery while specifying enhancements in knowledge generation, clinical application and policy change domains likely to assist with achievement of best practices by 2020.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".