Using Evidence To Inform Practice and Policy To Enhance the Quality of Care for Persons with Traumatic Spinal Cord Injury
Bibliographic record
Abstract
In today's economic climate, there is a need to demonstrate a return on investment for healthcare spending and for clinical practice and policy to be informed by evidence. Navigating this process is difficult for decision-makers, clinicians, and researchers alike. This article will describe how a knowledge translation framework and an evidence-based policy-making process were integrated to clarify the problem, frame options, and plan implementation, to impact clinical practice and policy in the area of traumatic spinal cord injury (tSCI). The Access to Care and Timing (ACT) project is focused on optimizing the access and timing of specialized healthcare delivery for persons sustaining a tSCI in Canada. A simulation model was developed that uses current patient data to address complex problems faced by the healthcare system. At a workshop, participants stressed the importance of linking interventions to short- and long-term outcomes to drive change. Presently, there are no national, system level indicators to monitor performance after tSCI. Although the ideal system of care after tSCI is unknown, indicator collection will establish a baseline to measure improvement. The workshop participants prioritized two indicators important from the clinician and patient perspective-timely admission to rehabilitation and meaningful community participation. The ACT simulation model for tSCI care will be used to promote the uptake of identified indicators and provide a predictive link between interventions on potential outcomes. The standardized collection of outcome-oriented indicators will help to evaluate the access and timing of care and to define the ideal system of care after SCI.
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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 | 0.192 | 0.422 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".