COVID-19’s impact on a community-based physical activity program for adults with moderate-to-severe TBI
Bibliographic record
Abstract
Purpose Physical activity (PA) is proposed for long-term problems after traumatic brain injury (TBI) with mood, quality of life, and participation. However, COVID-19 mitigation strategies resulted in widespread closures of community-based fitness centres, including one housing a peer-assisted PA program (TBI-Health). The purpose of this study was to provide an in-depth exploration of COVID-19’s impact on the TBI-Health program for adults with moderate-to-severe TBI and determine how their PA behaviours could be supported in the pandemic.Methods Interpretative phenomenological analysis was employed to collect and analyze data from semi-structured Zoom-facilitated interviews with seven female and nine male adults with moderate-to-severe TBI (including program participants and mentors).Results Three major themes were identified. Need for PA after TBI included specific benefits of PA after TBI and desire for an adapted PA program. Lasting Impacts of the TBI-Health Program identified belonging to the TBI-Health community, benefits, and knowledge transfer from the program. Resilience and Loss through the Pandemic comprised the repercussions of COVID-19, loss of the PA program, adapting PA to the pandemic, and resilience after TBI.Conclusion This study provides insights about impacts of participating in community-based peer-assisted PA programs after moderate-to-severe TBI and ways to support PA in unforeseen circumstances.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".