Post Doc Competition (Health Services, Economics and Policy Change) ID 1985179
Bibliographic record
Abstract
Background/Objectives The COVID-19 pandemic has significantly impacted healthcare utilization; however, research has not investigated the impact in the spinal cord injury/dysfunction (SCI/D) population in Canada. To examine healthcare utilization and delivery during the COVID-19 pandemic in individuals with SCI/D. Methods/Overview A repeated-cross sectional retrospective longitudinal cohort study design was conducted using health administrative database in Ontario, Canada. In 5,754 individuals with SCI/D, healthcare utilization and delivery (in-person, and virtual) were determined at the 1) pre-pandemic (March 2015 to February 2020), 2) initial pandemic onset (March 2020-May 2020), and 3) pandemic (June 2020 to March 2022) phases. Autoregressive integrated moving average (ARIMA) modelling were conducted to determine pandemic impact on monthly healthcare utilization and delivery. Results The initial pandemic onset period had a significant reduction of 24% in physician (p=0.0081), 35% in specialist (p<0.0001), and 30% in urologist (p<0.0001) visits, compared to pre-pandemic levels, with a partial recovery as the pandemic progressed. In April 2020, compared to the pre-pandemic period, a significant increase (p<0.0001) for virtual visits for physician, specialist, urologist, and primary care was found. The initial pandemic onset period had a 46% decrease in ED visits (p=0.0764) and 58% decrease in hospital admissions (p=0.0011), compared to the pre-pandemic period. Conclusion Healthcare utilization dropped in the initial pandemic onset period as physician, specialist, urologist, and ED visits, as well as hospitalization decreased significantly (p<0.05) versus pre-pandemic levels. Virtual visit increases compensated for in-person visit decreases as the pandemic progressed to allow for total visits to partially recover.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.597 | 0.163 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".