Optimizing spinal cord injury care in Canada: Development of a framework for strategy and action
Bibliographic record
Abstract
National health strategies are integral in defining the vision and strategic direction for ensuring the health of a population or for a specific health area. To facilitate a national coordinated approach in spinal cord injury (SCI) research and care in Canada, Praxis Spinal Cord Institute, with support from national experts and funding from the Government of Canada, developed a national strategy to advance SCI care, health, and wellness based on previous SCI strategic documents. This paper describes the development process of the SCI Care for Canada: A Framework for Strategy and Action. Specifically, it covers the process of building on historical and existing work of SCI in Canada through a thorough review of literature to inform community consultations and co-creation design. Furthermore, this paper describes planning for communication, dissemination, and evaluation. The SCI Care Strategic Framework promotes an updated common understanding of the goals and vision of the SCI community, as well as strengths and priorities within the SCI system regarding care, health, and wellness. Additionally, it supports the coordination and scaling up of SCI advancements to make a sustainable impact nationwide focusing on the needs of people living with SCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| 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".