Describing the current state of post-rehabilitation health system surveillance in Ontario – an invited review
Bibliographic record
Abstract
Context: Spinal cord injury (SCI) presents numerous physiological, psychosocial, and environmental complexities resulting in significant healthcare system resource demands.Objective: To describe the current health system surveillance mechanisms in Ontario, Canada and highlight gaps in health surveillance among adults with SCI across their lifespan.Methods: A review of administrative data sources capturing SCI-specific information took place via internet searching and networking among SCI rehabilitation and health services experts with emphasis on functionality, health service utilization, and quality of life data.Results: The review identified a distinct paucity of data elements specific to the health surveillance needs of individuals with SCI living in the community. The gaps identified are: (1) a lack of data usability; (2) inadequate linkage between available datasets; (3) inadequate/infrequent reporting of outcomes; (4) a lack of relevant content/patient-reported outcomes; and, (5) failure to incorporate additional data sources (e.g. Insurance datasets).Conclusion: Currently, SCI-specific health data is disproportionately weighted towards the first 3–6 months post injury with detailed data regarding pre-hospital care, acute management and rehabilitation, but little existing infrastructure supporting community-based health surveillance. Given this reality, the bolstering of meaningful community health surveillance of this population across the lifespan is needed. Addressing the identified gaps in health surveillance must inform the creation of a comprehensive community health dataset incorporating patient-reported outcome measures and enabling linkage with existing administrative and/or clinical databases. A future harmonized data surveillance strategy would, in turn, positively impact function, health services, resource utilization and health-related quality of life surveillance.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".