Co-designing a survey on computer-mediated communication use among adolescents with acquired brain injury: evaluating participant experiences in a mixed-methods study
Bibliographic record
Abstract
Background Computer-mediated communication (CMC) - using technology to engage with others via digital platforms (e.g. social media) - is an integral mode of social interaction for adolescents. Integrating CMC into rehabilitation could benefit adolescents with acquired brain injury (ABI), yet its use is under-studied. This study aimed to address this gap by co-designing a survey on CMC use among adolescents with ABI and evaluating the project team's perceptions of engagement in the co-design process post-study. Methods The project team comprised 10 interest-holders: youth with ABI (n =2), rehabilitation professionals (n =2), researchers (n =5), and a family member (n =1). Survey co-design sessions conducted via videoconferencing were guided by the Double Diamond (DD) Framework. Mixed-methods analysis included descriptive statistics from the Patient and Public Engagement Evaluation Tool (PPEET), qualitative insights on team engagement and reflexive thematic analysis of memos, and overview of survey categories and questions. Results Five co-design sessions resulted in the Social Media Building Blocks (SMBB) survey, refined through piloting with two youth with ABI and a health literacy review. PPEET data showed strong team engagement, with 5/6 'strongly agreeing' their views were heard and valued. Thematic analysis of team memos identified three themes: valuing diverse perspectives, enthusiastic engagement in co-design, and the importance of reflexivity. The final SMBB survey included four question categories: participation in online communication, accessibility, post-ABI communication experiences, and desired supports. Conclusions Project team members valued the collaborative survey design phases and engaging youth with ABI. Findings highlight co-design frameworks' potential to enhance engagement in rehabilitation research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".