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Record W4312129349 · doi:10.1111/1460-6984.12826

Do speech–language therapists support young people with communication disability to use social media? A mixed methods study of professional practices

2022· article· en· W4312129349 on OpenAlexaff
Nichola Shelton, Natalie Munro, Melanie Keep, Julia Starling, Lyn Tieu

Bibliographic record

VenueInternational Journal of Language & Communication Disorders · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThematic analysisPsychologySocial mediaMedical educationQualitative researchApplied psychologyMedicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Social media is increasingly used by young people, including those with communication disability. To date, though, little is known about how speech-language therapists (SLTs) support the social media use of young people with communication disability. AIMS: To explore what services SLTs provide to facilitate the social media use of young people with communication disability, including what these services look like, and the factors that impact SLTs' professional practices. METHODS & PROCEDURES: A sequential mixed methods approach was employed including an online survey and in-depth semi-structured interviews. Participants were qualified practising SLTs in Australia with a caseload that included clients aged 12-16 years. Quantitative data were analysed with SPSS. A thematic analysis of qualitative data was conducted with NVivo. OUTCOMES & RESULTS: Survey responses from 61 SLTs were analysed. Interviews were conducted with 16 participants. Survey data indicated that SLTs do not systematically assess or treat young people's use of social media as part of their professional practice. Interview data revealed that where SLTs do support young people's use of social media, they transfer knowledge and practices typically used in offline contexts to underpin their work supporting clients' use of social media. In terms of factors that affect SLTs' practices, three major themes were identified: client/family factors, SLT factors, and societal factors. CONCLUSIONS & IMPLICATIONS: While young people with communication disability may desire digital participation in social media spaces, SLTs' current professional practices do not routinely address this need. Professional practice guidelines would support SLTs' practices in this area. Future research should seek the opinions of young people with communication disability regarding their use of social media, and the role of SLTs in facilitating this. WHAT THIS PAPER ADDS: What is already known on the subject Young people with communication disability use social media, but digital inequality means that they may not do so to the same extent as their typically developing peers. Services targeting a young person's social media use is within the SLT scope of practice. Whether or not SLTs routinely address the social media use of young people with communication disability as part of their professional practice is unknown. What this study adds to existing knowledge This study found that SLTs in Australia do not systematically provide professional services targeting young people's use of social media. When services do address a young person's use of social media, knowledge and practices typically used by SLTs in offline contexts are adapted to support their work targeting online social media contexts. What are the potential or actual clinical implications of this work? This study indicates that SLTs should consider a range of factors when deciding whether to address a young person's social media use. Adapting existing offline professional practices to online environments could support SLTs' work in providing services targeting social media use. Professional practice guidelines would support SLTs' work facilitating the social media use of young people with communication disability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.069
GPT teacher head0.513
Teacher spread0.444 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

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