The impact of the COVID-19 virus and pandemic on healthcare utilization, access, delivery, experiences, and outcomes in the spinal cord injuries/dysfunction population: A scoping review study
Bibliographic record
Abstract
BACKGROUND: Individuals with spinal cord injuries or disease (SCI/D) require frequent healthcare services. The COVID-19 pandemic may have impacted healthcare. Furthermore, due to secondary health conditions and comorbidities persons with SCI/D are at increased risk of experiencing severe symptoms or outcomes if infected with the COVID-19 virus. It is unclear to what extent research has investigated the pandemic and virus impacts on the SCI/D population. OBJECTIVE: To identify and summarize what is reported in the literature on the impact the COVID-19 virus and pandemic had on healthcare, health outcomes, and experiences in the adult SCI/D population. METHODS: Electronic databases and grey literature were searched for articles that included an adult population with a SCI/D and investigated the impact the COVID-19 virus and pandemic had on healthcare-related outcomes and experiences. Articles were double screened, and data were extracted, and synthesized to provide a descriptive summary of the findings. RESULTS: Twenty-four studies were included in this review with eight qualitative, fifteen quantitative, and one mixed methods study. Sixteen studies investigated healthcare utilization/access; nine investigated care delivery, nine investigated patient outcomes, and eight investigated patient experiences, with multiple studies spanning different categories of investigation. The pandemic was detrimental to healthcare utilization, access, and outcomes, but no studies quantified these changes. Virtual care was well-received by the SCI/D population to maintain continuity of care. The SCI/D population had issues with maintaining caregiving support. It was unclear if the COVID-19 virus infection impacted individuals with SCI/D differently than the general population. CONCLUSIONS: This scoping review found the pandemic negatively impacted multiple aspects of healthcare in individuals with SCI/D, however further investigation on health outcomes is required. More research, particularly large-scale quantitative studies, investigating healthcare access, utilization, and delivery, as well as patient outcomes and experiences is needed to improve care in the SCI/D population post-pandemic onset.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".