Evaluating the quality of evidence for gaming disorder: A summary of systematic reviews of associations between gaming disorder and depression or anxiety
Bibliographic record
Abstract
Gaming disorder has been described as an urgent public health problem and has garnered many systematic reviews of its associations with other health conditions. However, review methodology can contribute to bias in the conclusions, leading to research, policy, and patient care that are not truly evidence-based. This study followed a pre-registered protocol (PROSPERO 2018 CRD42018090651) with the objective of identifying reliable and methodologically-rigorous systematic reviews that examine the associations between gaming disorder and depression or anxiety in any population. We searched PubMed and PsycInfo for published systematic reviews and the gray literature for unpublished systematic reviews as of June 24, 2020. Reviews were classified as reliable according to several quality criteria, such as whether they conducted a risk of bias assessment of studies and whether they clearly described how outcomes from each study were selected. We assessed possible selective outcome reporting among the reviews. Seven reviews that included a total of 196 studies met inclusion criteria. The overall number of participants was not calculable because not all reviews reported these data. All reviews specified eligibility criteria for studies, but not for outcomes within studies. Only one review assessed risk of bias. Evidence of selective outcome reporting was found in all reviews-only one review incorporated any of the null findings from studies it included. Thus, none were classified as reliable according to prespecified quality criteria. Systematic reviews related to gaming disorder do not meet methodological standards. As clinical and policy decisions are heavily reliant on reliable, accurate, and unbiased evidence synthesis; researchers, clinicians, and policymakers should consider the implications of selective outcome reporting. Limitations of the current summary include using counts of associations and restricting to systematic reviews published in English. Systematic reviewers should follow established guidelines for review conduct and transparent reporting to ensure evidence about technology use disorders is reliable.
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 | 0.134 | 0.439 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.033 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".