Perceptions of the influence of computer-mediated communication on the health and well-being of early adolescents
Bibliographic record
Abstract
Recent technological advances have provided many youth with daily, almost continuous cell-phone and Internet connectivity through portable devices. Young people's experiences with computer-mediated communication (CMC) and their views about how this form of communication affects their health have not been fully explored in the scientific literature. A purposeful maximum variation sample of young people (aged 11-15 years) across Ontario was identified, using key informants for recruitment. The young people participated in seven focus groups (involving a total of 40 adolescents), and discussed various aspects of health including the health impacts of CMC. Inductive content analysis of the focus group transcripts revealed two overarching concepts: first, that the relationship between health and the potential impacts of CMC is multidimensional; and secondly, that there exists a duality of both positive and negative potential influences of CMC on health. Within this framework, four themes were identified involving CMC and: (1) physical activity, (2) negative mental and emotional disturbance, (3) mindfulness, and (4) relationships. With this knowledge, targeted strategies for healthy technology use that draw on the perspectives of young people can be developed, and can then be implemented by parents, teachers, and youth themselves.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".