Application of Visualization-based Analytic Methods in Population Health and Health Services Research to Rehabilitation Sciences
Bibliographic record
Abstract
Visualization-based analytic methods have evolved rapidly over the last two decades, allowing knowledge generation from large healthcare datasets. However, their application in the rehabilitation sciences, particularly related to population health and health services research is limited. This research is divided into two main parts. The first presents two systematic literature syntheses of two information visualization methods, visual analytics and interactive visualization, both applied in these areas of healthcare. The second part presents two use cases illustrating the application of these methods. The first use case is an interactive visualization dashboard prototype for the Canadian Institute for Health Information's Population Grouping Methodology, where the candidate was placed as an embedded fellow. The second use case presents a proof-of-principle dashboard exploring data on rehospitalizations from a multicenter practice-based evidence study on outcomes of persons with spinal cord injury. Rehabilitation health services' research can benefit from the use of visualization-based methods alone or combined with exploratory visual and confirmatory statistical analysis to advance the field. In addition, this thesis highlights the opportunity for organizations to leverage embedded research for advancing and improving healthcare through integrated knowledge translation initiatives and building Learning Health Systems inclusive of rehabilitation services.
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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 | 0.073 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".