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Record W2991341937 · doi:10.1080/0886571x.2019.1696263

“Double-edged sword” of Digital Media Use among Youth in Residential Treatment: Perspectives of Service Providers

2019· article· en· W2991341937 on OpenAlexaff
Bethany Good, Faye Mishna

Bibliographic record

VenueResidential Treatment for Children & Youth · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPublic relationsDigital mediaSocial mediaScholarshipPromotion (chess)Service providerPsychological resilienceResilience (materials science)SociologyService (business)Internet privacyPsychologyBusinessPolitical scienceSocial psychologyMarketingComputer science

Abstract

fetched live from OpenAlex

The social significance of digital media and technology is incontestable, particularly with youth. While digital media use can offer youth in residential treatment (RT) opportunities, it also carries risks. Although there has been a shift in RT from a model fostering isolated, self-contained settings, toward the promotion of family and community integration, there is a dearth of scholarship addressing youth digital media use in RT. To address the gaps in research on digital media use among youth in RT and the absence of system-wide policies and formal consensus on guidelines for addressing the issue, the findings of the current study offer insight into the experiences of 25 service providers from four programs. The findings highlight that regardless of location or treatment approach, RT is addressing youth digital media use. There exists a continuum of approaches ranging from risk prevention to risk resilience. As social media have made prohibiting contact between youth impossible, digital media use has been a catalyst for programs to attend to youth relationships outside of the programs. Digital media have thus both introduced new challenges to supporting youth and facilitated a more contemporary resilience-oriented and ecologically informed the approach to treatment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.278
Teacher spread0.247 · 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
Published2019
Admission routes1
Has abstractyes

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