Effect of the <scp>COVID</scp>‐19 pandemic on individuals with spinal cord injury: Mental health and use of telehealth
Bibliographic record
Abstract
INTRODUCTION: Limited access to health care services and the self-isolation measures due to the coronavirus disease 2019 (COVID-19) pandemic may have had additional unintended negative effects, affecting the health of individuals with spinal cord injury (SCI). OBJECTIVES: To examine the perceived influence of the COVID-19 pandemic on individuals with SCI. First, this study looked to understand how the pandemic affected the use and perception of telehealth services for these individuals. Second, it investigated the effect of COVID-19 on mental health. DESIGN: Cross-sectional online survey. SETTING: Individuals with SCI living in the community in British Columbia, Canada. PATIENTS: This survey was offered to individuals with SCI and had 71 respondents, with 34% living in a rural setting and 66% in an urban setting. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Telehealth utility, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7 (GAD-7), Fear of COVID-19 scale (FCV-19S), and Perceived Vulnerability to Disease (PVD). RESULTS: Telehealth use in the SCI population has increased from 9.9% to 25.4% over the pandemic, with rates of telehealth use in urban centers nearing those of rural participants. Thirty-one percent of respondents had probable depression and 7.0% had probable generalized anxiety disorder as measured by a score of ≥10 on the PHQ-9 and GAD-7, respectively. The mean scores on FCV-19S and PVD were 17.0 (6.6 SD) and 4.29 (1.02 SD), respectively. CONCLUSION: Telehealth use during COVID-19 has more than doubled. It is generally well regarded by respondents, although only a fourth of the SCI population has reported its use. With this in mind, it is important to understand the barriers to further adoption. In addition, higher rates of probable depression were seen than those estimated by pre-pandemic studies in other countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".