The Application of Operations Research Methodologies to the Delivery of Care Model for Traumatic Spinal Cord Injury: The Access to Care and Timing Project
Bibliographic record
Abstract
The long-term impact of spinal cord injury (SCI) on the health care system imposes a need for greater efficiency in the use of resources and the management of care. The Access to Care and Timing (ACT) project was developed to model the health care delivery system in Canada for patients with traumatic SCI. Techniques from Operations Research, such as simulation modeling, were used to predict the impact of best practices and policy initiatives on outcomes related to both the system and patients. These methods have been used to solve similar problems in business and engineering and may offer a unique solution to the complexities encountered in SCI care delivery. Findings from various simulated scenarios, from the patients' point of injury to community re-integration, can be used to inform decisions on optimizing practice across the care continuum. This article describes specifically the methodology and implications of producing such simulations for the care of traumatic SCI in Canada. Future publications will report on specific practices pertaining to the access to specialized services and the timing of interventions evaluated using the ACT model. Results from this type of research will provide the evidence required to support clinical decision making, inform standards of care, and provide an opportunity to engage policymakers.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".