Impact of COVID-19 on helpline calls for activities related to technology overuse in Ontario, Canada
Bibliographic record
Abstract
Background: Video gaming revenues have increased dramatically during the COVID-19 pandemic. Trending social games and hyper-casual games are attracting new audiences that require further study. While engaging in video games and Internet-related behaviors inherently may help promote social connection and alleviate stress during the pandemic, a small proportion of individuals develop problematic habits that interfere with daily functioning. Therefore, the aim of this study is to examine the impact of COVID-19 lockdowns on the number of helpline calls for gaming disorder and problematic Internet use in the province of Ontario, Canada. Methods: Helpline calls were collected from a provincial mental health & addiction treatment service hotline from January 2019 to December 2021. This free and confidential service is for people who experience problems with alcohol, drugs, mental illness, and behavioral disorders. Growth modeling will be employed to examine the links between the number of calls received, the number of COVID-19 cases reported province-wide and the accumulated lockdown days across the different months. Results: The associations between the linear, quadratic and cubic growth/change curve factors of the number of calls received in relation to the progress of the pandemic will be reported for time variant, time-invariant and parallel growth moderators. Conclusions: Helpline calls are expected to increase during lockdowns and decrease when restrictions are lifted. This study serves to inform preventive measures that should be considered with the implementation of lockdown during a pandemic to prevent problematic forms of gaming or Internet use.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".