How are adolescents with acquired brain injury using computer-mediated communication?: a systematic review of the literature
Bibliographic record
Abstract
PURPOSE: To systematically review the literature on the use of computer-mediated communication (CMC) for social participation by adolescents with acquired brain injury (ABI), characterize patterns of use, perceived benefits and challenges, and existing supports for this population. METHODS: Following PRISMA guidelines, we searched seven databases (CINAHL, Ovid Medline, APA PsycINFO, Allied and Complementary Medicine Database (AMED), Embase, SpeechBITE and the Cochrane Database for Systematic Reviews) and grey literature from inception to January 2024, and hand-searched references. The PCC framework guided the inclusion of English-language articles on adolescents aged 13-18 with ABI, focusing on CMC use in community or outpatient settings. Quality of the included studies was assessed using the Critical Appraisal Skills Programme (CASP) checklists. Results were synthesized using thematic analysis. RESULTS: Nine studies met inclusion criteria. Thematic analysis identified five major themes: navigating adolescent ABI, the digital landscape, technology as a facilitator, leveraging CMC and parental involvement to support autonomy, and synergies for CMC within rehabilitation settings. CONCLUSION: CMC can support social participation for adolescents with ABI; however, significant knowledge gaps exist regarding access barriers and effective supports. Further research is needed to develop specialized training for rehabilitation professionals to support adolescents with ABI in accessing CMC safely.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".