RETRACTED ARTICLE: Insomnia, sleepiness, anxiety and depression among different types of gamers in African countries
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Gaming has increasingly become a part of life in Africa. Currently, no data on gaming disorders or their association with mental disorders exist for African countries. This study for the first time investigated (1) the prevalence of insomnia, excessive daytime sleepiness, anxiety and depression among African gamers, (2) the association between these conditions and gamer types (i.e., non-problematic, engaged, problematic and addicted) and (3) the predictive power of socioeconomic markers (education, age, income, marital status, employment status) on these conditions. 10,566 people from 2 low- (Rwanda, Gabon), 6 lower-middle (Cameroon, Nigeria, Morocco, Tunisia, Senegal, Ivory Coast) and 1 upper-middle income countries (South Africa) completed online questionnaires containing validated measures on insomnia, sleepiness, anxiety, depression and gaming addiction. Results showed our sample of gamers (24 ± 2.8 yrs; 88.64% Male), 30% were addicted, 30% were problematic, 8% were engaged and 32% were non-problematic. Gaming significantly contributed to 86.9% of the variance in insomnia, 82.7% of the variance in daytime sleepiness and 82.3% of the variance in anxiety [p < 0.001]. This study establishes the prevalence of gaming, mood and sleep disorders, in a large African sample. Our results corroborate previous studies, reporting problematic and addicted gamers show poorer health outcomes compared with non-problematic gamers.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".