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Record W2945381952 · doi:10.22158/jetss.v1n1p12

Social Media and 21st-Century Child and Youth Care Practice

2019· article· en· W2945381952 on OpenAlexaff
Tipenga Arago, Sadia Samar Ali, Tahany Dassouki, Simonne Massner, Dominique Mendiola, Jennifer Po, Kevin Sun, Gerard Bellefeuille

Bibliographic record

VenueJournal of Education Teaching and Social Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSocial mediaThematic analysisPacePublic relationsSpace (punctuation)PsychologyQualitative researchSociologyMedical educationMedicinePolitical scienceWorld Wide WebSocial scienceComputer science

Abstract

fetched live from OpenAlex

The exponential growth of online information and communication technologies such as texting and social media sites like Facebook, Instagram, and Snapchat have opened up new possibilities for child and youth care (CYC) practitioners to engage with children, youth, and families. Yet very little is known about the therapeutic use of these technologies as a direct CYC practice method. Hence, this qualitative, course-based research project aimed to explore the use of social media as a therapeutic practice by CYC students at MacEwan University. The Instagram social networking site was used to collect data. A thematic analysis identified four overarching themes: (a) a relationship, engagement, and communication building tool, (b) a source of support and information, (c) a creative space for self-expression, and (d) a safe place. The results of this course-based study indicate that CYC students use social media responsibly in their work with children, youth, and families. The use of social media appears to have a positive impact and beneficial use in CYC as long as it is utilized and maintained in a professional manner. More research into social media is, however, required to help CYC practitioners keep pace with information and communication technologies and become well-informed about their use and misuse.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.352
Teacher spread0.322 · 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 designQualitative
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

Citations2
Published2019
Admission routes1
Has abstractyes

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