From screen time to the digital level of analysis: a scoping review of measures for digital media use in children and adolescents
Bibliographic record
Abstract
OBJECTIVES: This scoping review aims to facilitate psychometric developments in the field of digital media usage and well-being in young people by (1) identifying core concepts in the area of "screen time" and digital media use in children, adolescents, and young adults, (2) synthesising existing research paradigms and measurement tools that quantify these dimensions, and (3) highlighting important areas of need to guide future measure development. DESIGN: A scoping review of 140 sources (126 database, 14 grey literature) published between 2014 and 2019 yielded 162 measurement tools across a range of domains, users, and cultures. Database sources from Ovid MEDLINE, PsycINFO and Scopus were extracted, in addition to grey literature obtained from knowledge experts and organisations relevant to digital media use in children. To be included, the source had to: (1) be an empirical investigation or present original research, (2) investigate a sample/target population that included children or young persons between the ages of 0 and 25 years of age, and (3) include at least one assessment method for measuring digital media use. Reviews, editorials, letters, comments and animal model studies were all excluded. MEASURES: Basic information, level of risk of bias, study setting, paradigm, data type, digital media type, device, usage characteristics, applications or websites, sample characteristics, recruitment methods, measurement tool information, reliability and validity. RESULTS: Significant variability in nomenclature surrounding problematic use and criteria for identifying clinical impairment was discovered. Moreover, there was a paucity of measures in key domains, including tools for young children, whole families, disadvantaged groups, and for certain patterns and types of usage. CONCLUSION: This knowledge synthesis exercise highlights the need for the widespread development and implementation of comprehensive, multi-method, multilevel, and multi-informant measurement suites.
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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 | 0.043 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".