Responding to the ongoing pandemic-related challenges of individuals with brain injury through the perspective of community-service in Canada: A qualitative study
Bibliographic record
Abstract
OBJECTIVE: To investigate brain injury (BI) associations' perspectives regarding the impacts of the second year of the COVID-19 pandemic on individuals with BI and BI associations services across Canada. METHODS: This qualitative descriptive study included 26 representatives of Canadian BI associations that participated in six online focus groups to discuss the effects of the second year of the pandemic on clients living with BI and on the provision of community services. RESULTS: Findings revealed three main themes: 1) ongoing pandemic-related challenges faced by clients living with BI, including worsening mental health and basic needs insecurities, difficulties faced by clients in adhering to safety measures, and ongoing technological issues; 2) ongoing adaptations to accommodate clients' needs, including offering tailored services, ensuring consistent and transparent safety measures, and providing hybrid services; and 3) developing a sustainable 'new normal' aligned with association mandates and resources by expanding networks and building resilience. CONCLUSION: The unfolding of the pandemic has brought increased challenges for people with BI and reinforced the need for adapted, clear, and accessible public health information to ensure the safety of vulnerable populations in times of crisis. It is essential to bolster community-based associations that provide direct care to people with BI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".