Youth health in a digital world: Approaching screen use in clinical practice
Bibliographic record
Abstract
New technologies, such as smartphones, have altered our behaviours and cultural structures more dramatically than televisions of our past. The array of today's electronic devices have pulled our eyes closer to the screens and our focus further into the boxes behind those screens. Screens may serve us; simultaneously, they are increasingly giving rise to health and social challenges that researchers are only beginning to understand. There is a growing dis-ease among parents and health care providers (HCPs) about how screens are affecting youth. As the push for increased screen time continues in both educational and workplace settings, HCPs are not only tasked with helping parents and youth cope, but they must find ways to manage the impact of increased personal and professional screen time on their own wellbeing. This article considers the impact of increased screen time on two groups: youth and the HCPs supporting them. Furthermore, the authors explore the impact of screen use on clinical interactions, and patient care, suggesting a process for addressing screen use and provide specific tools including a reflective query for HCPs to better evaluate the impact of their own screen usage, 'the Coaching Stance' and TGROW, a questioning approach derived from coaching theory.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".