Understanding the Early Impacts of the COVID-19 Pandemic on Brain Injury Associations Across Canada: A Qualitative Study
Bibliographic record
Abstract
The COVID-19 pandemic has increased challenges for people living with brain injury and community associations to support this vulnerable population. This study aimed to gain an in-depth understanding of the challenges faced by brain injury survivors during the first year of the pandemic and how community brain injury associations adapted their services to respond to these needs. Findings from seven focus-group with 31 representatives of Canadian brain injury associations revealed 4 main themes: (1) Addressing evolving client needs; (2) Keeping clients safe; (3) Challenges and opportunities navigating the digital world; and (4) Sustaining brain injury associations in the face of uncertainties and disruptions. To comply with public health measures, associations reported pivoting their service delivery online, despite recognizing the difficulties this could create for many brain injury survivors in accessing and using technology. Our findings also highlight concrete directions for not-profit organizations providing instrumental help with activities, acting as a liaison and interpreter of public health guidelines, and in connecting with clients using technology while handling potential cognitive and technological challenges. Addressing these issues has the potential to protect people living with brain injury and community associations from external threats, like pandemics, in the future.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".