Impacts of the COVID-19 pandemic on people with spinal cord injury.
Bibliographic record
Abstract
PURPOSE: Few studies have examined the impacts of the COVID-19 pandemic on the lives of people with spinal cord injury (SCI), a population uniquely vulnerable to pandemic-related stressors. This study examines the impact of the pandemic on three life domains (psychosocial health, health and health behavior, and social participation) and identifies risk factors for adverse psychosocial health impacts in a sample of people with SCI. METHOD: A diverse sample of 346 adults with SCI completed a survey assessing demographic, disability, health, and social characteristics, and perceived impacts of the pandemic. RESULTS: Many respondents reported no change on items reflecting psychosocial health, health and health behavior, and social participation; however, among those reporting change, more reported negative than positive impacts. Negative impacts were most striking with regard to psychosocial health and social engagement, with approximately half reporting a worsening of stress, depression, anxiety, and loneliness and a reduction in face-to-face interactions and participation in life roles. Regression analyses revealed that those at greater risk of adverse psychosocial impacts were women, were non-Black, were in poorer health, had greater unmet care needs, and were less satisfied with their social roles and activities. CONCLUSIONS: Although not universal, negative impacts were reported by many respondents 9-15 months into the pandemic. Future research should examine the impacts of the pandemic over time and on a wider range of outcomes. Such research could generate substantial benefits in understanding, preventing, or minimizing the adverse effects of the evolving pandemic and future public health emergencies in people with SCI. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".