Evaluation of the Quality of Current COVID-19 Resources Developed for Individuals with Spinal Cord Injuries: A Scoping Review
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, there was an urgent need for information on dealing with it among people with spinal cord injuries (SCI). Organizations provided resources, but many of them were generic. In some cases, the information was provided by dubious sources, contradictory, or not assessed for usability with individuals with SCI. This study reviewed COVID-19 web-based resources for individuals with SCI and evaluated their quality. Methods: A scoping review for COVID-19-related web-based resources for individuals with SCI was performed by first identifying SCI-relevant organizations and, subsequently, targeted website searching using a systematic search strategy in May 2021. The included resources were categorized based on their content and format (e.g., video, infographic, text). The resources were evaluated using tools that had been previously validated. Results: Our search identified 71 SCI organizations and 10,538 potential resources. Based on inclusion and exclusion criteria, 112 resources were included and categorized based on their content into ten main domains: prevention, caregivers, exercise, mental health, stories, telehealth, specific organs/systems, report of evidence, SCI network COVID-19 response and COVID-19 communication rights toolkit. The average score for the quality of the text, infographic, and video resources are 9.72/28 (Range:3-24), 37.75/44 (Range:35-41), and 59.14/80 (Range: 49-75), respectively. Conclusion: Website resources mainly focused on preventing COVID-19. Only five of them addressed telehealth during COVID-19 for individuals with SCI. The results of this study will inform the development of SCI-oriented toolkits for future pandemics.
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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 | 0.085 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.042 | 0.035 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".