How Can Policymakers be Encouraged to Support People With Spinal Cord Injury—Scoping Review
Bibliographic record
Abstract
STUDY DESIGN: Scoping review. OBJECTIVE: Regarding that inappropriate medical care approaches, absence of rehabilitation services, and existing barriers in physical, social, and policy environments lead to poor outcomes in individuals with spinal cord injury (SCI) and provision for appropriate interventions and care must be created by health policymakers, we conducted this scoping review to investigate how policymakers can be persuaded to set new plans for individuals with SCI. METHODS: This review was performed according to Arksey and O'Malley's framework. PubMed was searched in February2019 without language limitation. We looked for other potential gray literature sources and some professional websites. References sections of selected articles were also scanned for other relevant literature. RESULTS: We included literature that met inclusion criteria to answer our research question. The literature was divided into 3 categories. The first category included economic impact of SCI. The second category included the role of research and developing research strategy. The third category included effective interaction and communication with policymakers. CONCLUSION: It is essential to consider multiple factors for influencing policymakers' decisions. These factors include knowing how to communicate with policymakers and presenting constructive ideas, providing a source of valid, reliable, and consistent data, considering the role of patients' advocacy groups and Non-Governmental Organizations (NGOs), and presentation of the importance of early intervention in reducing healthcare system costs. Ultimately, the goal is to have a comprehensive and flexible plan for influencing policymakers.
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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 | 0.080 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".