Social Media and 21st-Century Child and Youth Care Practice
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".