A qualitative study of individuals with acquired brain injury’ and program facilitators’ experiences in virtual acquired brain injury community support programs
Bibliographic record
Abstract
Purpose Acquired brain injury (ABI) community support programs aim to help support long-term needs through informational and psychosocial support. Due to the COVID-19 pandemic, many support programs adopted virtual program delivery. However, the experiences of facilitators and people with ABI who participate in virtual support programs are understudied. This study aimed to describe the experiences of people with ABI and program facilitators participating in virtual ABI community support programs.Materials and Methods This was a qualitative descriptive study. Semi-structured interviews were conducted with people with ABI and program facilitators who participated in virtual ABI community support programs. Data were analyzed using inductive thematic analysis.Results In total, 16 participants were included in this study. Of the 16 participants, 14 were people with ABI (three of whom were also program facilitators) and two were program facilitators without ABI. Our analysis generated three themes including perceived benefits (theme 1), perceived challenges (theme 2), and considerations to improve program quality (theme 3). Each theme outlines subthemes relaying the experiences of participants.Conclusions These findings highlight the need for stakeholders to implement guidelines and training for program facilitators and attendees of virtual ABI support programs to maximize accessibility, usability, inclusivity and safety.IMPLICATIONS FOR REHABILITATIONThis study described the experiences of people with acquired brain injury and facilitators who participated in virtual support programs.Benefits of virtual support programs include connecting with peers, increased access to resources, and enhanced feasibility and accessibility.Difficulties with virtual support programs include intrapersonal (e.g., increased side effects), interpersonal (e.g., communication barriers), and environmental and contextual (e.g., privacy concerns) challenges.Suggestions to improve program quality include creating a safe and respectful environment, fostering engagement and managing challenging situations, and enhancing accessibility and inclusivity.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 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".