Impacts of digital technologies on child and adolescent health: recommendations for safer screen use in educational settings
Bibliographic record
Abstract
The use of screen-based digital technologies (such as computers and digital devices) is increasing for children and adolescents, worldwide. Digital technologies offer benefits, including educational opportunities, social connection and access to health information. Digital fluency has been recognised as an essential skill for future prosperity. However, along with these opportunities, digital technologies also present a risk of harm to young people. This issue may be particularly important for young New Zealanders, who have among the highest rates of screen use in the world. Our recently published review examined the impacts of digital technologies on the health and wellbeing of children and adolescents. Key findings revealed some positive impacts from moderate use of digital technologies; however, frequent and extended use of screen-based digital tools were associated with negative impacts on child and adolescent health in some areas, such as eye health, noise-induced hearing loss and pain syndromes. Conversely, in areas such as mental health, wellbeing and cognition, quality of screen media content and additional factors such as age may be more important than duration of use. These challenges gave us the impetus to develop pragmatic recommendations for the use of digital technologies in schools, kura kaupapa and early childhood education. Recommendations include interventions to lower risk across different ages and stages of development. Supporting young people to mitigate risk and develop safer screen behaviours will allow them to gain essential digital skills and access opportunities that will enable them to thrive.
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 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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".