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Record W4392638306 · doi:10.1089/cyber.2023.0211

Does Facebook Use Provide Social Benefits to Adults with Traumatic Brain Injury?

2024· article· en· W4392638306 on OpenAlexaff
Catalina L. Toma, Juwon Hwang, Lisa Kakonge, Emily Morrow, Lyn S. Turkstra, Bilge Mutlu, Melissa C. Duff

Bibliographic record

VenueCyberpsychology Behavior and Social Networking · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMcMaster University
FundersNational Center for Medical Rehabilitation ResearchAgency for Healthcare Research and QualityNational Institutes of Health
KeywordsSocial connectednessClosenessPsychologyTraumatic brain injuryPreferenceSocial mediaContext (archaeology)DisconnectionPopulationCompensation (psychology)Clinical psychologySocial network (sociolinguistics)Social psychologyPsychiatryMedicineComputer science

Abstract

fetched live from OpenAlex

Drawing on the social compensation hypothesis, this study investigates whether Facebook use facilitates social connectedness for individuals with traumatic brain injury (TBI), a common and debilitating medical condition that often results in social isolation. In a survey (N = 104 participants; n = 53 with TBI, n = 51 without TBI), individuals with TBI reported greater preference for self-disclosure on Facebook (vs. face-to-face) compared to noninjured individuals. For noninjured participants, a preference for Facebook self-disclosure was associated with the enactment of relational maintenance behaviors on Facebook, which was then associated with greater closeness with Facebook friends. However, no such benefits emerged for individuals with TBI, whose preference for Facebook self-disclosure was not associated with relationship maintenance behaviors on Facebook, and did not lead to greater closeness with Facebook friends. These findings show that the social compensation hypothesis has partial utility in the novel context of TBI, and suggest the need for developing technological supports to assist this vulnerable population on social media platforms.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.354
Teacher spread0.312 · 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 designOther design
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

Citations3
Published2024
Admission routes1
Has abstractyes

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