Bibliographic record
Abstract
Technology addiction is an emerging issue that can present itself in many ways. It is characterized by excessive and obsessive use of any form of technology. First identified by the World Health Organization as a public health concern in 2015, discussions of the dangers of excessive technology use have only risen. By 2015, 46.4% of the world’s population was online, and this number has only grown (Zheng et al., 2016). As young people’s technology use increases (Statistics Canada, 2021), it is crucial to examine the health implications of this phenomenon. This literature review sought to explore the impacts of technology addiction on the mental health of youth populations in North America. It focuses on defining the ways technology addiction can present itself, the impacts of the COVID-19 pandemic, and physical and mental health, while placing emphasis on these factors with respect to young people. Review of existing literature suggests that gaming disorder, problematic social media use and excessive internet use are of particular concern post-pandemic and have concerning impacts on both physical and mental health. These issues are exacerbated in youth, whose technology use has risen in recent years. As technology use increases and addiction takes on novel forms, it is vital that pathology is standardized to allow treatment pathways to be created and ensure proper diagnosis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".