COVID-19, Loneliness and Technological Addiction: Longitudinal Data
Bibliographic record
Abstract
The COVID-19 outbreak has been shown to have had a negative impact on mental health: furthermore, lockdown measures may have increased levels of loneliness and addiction. Preliminary studies indicated an increased frequency of gaming and social media use during this time and augmented levels of loneliness likely account for the increase in gaming and social media addiction during lockdown. We conducted a longitudinal study administering a battery of self-report questionnaires at the beginning of lockdown (Stage 1) and three days before the end of the lockdown (Stage 2). Specifically, we measured loneliness feelings, frequency of gaming, and social media use, as well as both gaming and social media addiction. Data were analysed using Structural Equation Modeling. We found that loneliness levels longitudinally predicted both gaming and social media addiction, even when controlling for gaming and social media use at Stage 1. Increased feelings of loneliness, a well-known risk factor for gaming, along with social media addiction, may be a central variable heightening vulnerability to the onset or the maintenance of technological addiction during forced social isolation. Thus, future preventive interventions may want to target this issue.RésuméL’éclosion de la COVID-19 a eu un effet nuisible sur la santé mentale. De plus, les mesures de confinement ont intensifié la solitude et les addictions. Les études préliminaires indiquent une augmentation de la fréquence d’utilisation des jeux vidéo et des médias sociaux pendant cette période. Cette hausse est probablement attribuable à l’accroissement de la solitude. Nous avons mené une étude longitudinale à l’aide d’une batterie de questionnaires d’autoévaluation, qui ont été remplis au début du confinement (étape 1) et trois jours avant la fin du confinement (étape 2). En particulier, nous avons mesuré le sentiment de solitude, la fréquence d’utilisation des jeux vidéo et des médias sociaux, et la dépendance aux jeux vidéo et aux médias sociaux. Nous avons eu recours à la modélisation par équation structurelle pour analyser les données. Nous avons observé que le niveau de solitude est un prédicteur longitudinal de la dépendance aux jeux vidéo et aux médias sociaux, en contrôlant par l’effet de l’utilisation des jeux vidéo et des médias sociaux observée à l’étape 1. Le sentiment accru de solitude, qui est un facteur de risque connu de dépendance aux jeux vidéo et aux médias sociaux, pourrait être une variable centrale de l’accroissement de la vulnérabilité au développement ou à la persistance d’une dépendance à la technologie dans un contexte d’isolement social imposé. Par conséquent, les interventions à venir en matière de prévention pourraient cibler ce problème.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".