Experiences of Individuals Living with Spinal Cord Injuries (SCI) and Acquired Brain Injuries (ABI) during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic presents unique challenges for people living with acquired neurological conditions. Due to pandemic-related societal restrictions, changes in accessibility to medical care, equipment, and activities of daily living may affect the mental health of individuals with a SCI or ABI. This study aimed to understand the impact of the pandemic on psychological wellbeing, physical health, quality of life, and delivery of care in persons living with SCI and ABI. A secondary objective included exploring the use of virtual services designed to meet these challenges. In a companion study, participants were surveyed using validated scales of psychosocial health, physical health and healthcare access. In this study, 11 individuals gathered from the survey participated in virtual individual semi-structured interviews to provide accounts of lived experiences regarding critical health challenges and eHealth. Two researchers independently coded interviews for themes using a hermeneutic phenomenological approach. Through analysis of interviews, 5 themes were identified regarding COVID-19 and recovery, access to care, virtual healthcare, systemic barriers, and coping. Overall, limited opportunities due to the pandemic led to a need for adaptation and multifaceted outcomes on one’s wellbeing, which provides guidance for future clinical practice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".