Promoting Mental Wellbeing: Young Adults’ Experience on TikTok during the COVID-19 Pandemic Lockdown in Nigeria
Bibliographic record
Abstract
From the last quarter of 2019, the world witnessed the emergence of the COVID -19 virus that shook it to its knees, and Nigeria is not an exception. While countries were struggling with strategies on how to manage the virus, the lockdown option became paramount. During the period of the lockdown in Nigeria, most persons, especially young people, could not visit places of their choice. Hence, social media became their source of solace. This study examines the experiences of young adults in using TikTok to minimise the negative effect of isolation during the COVID-19 lockdown in Nigeria. The authors interviewed ten young persons between the ages of 19 to 31. A thematic analysis of the interviews using Braun and Clark (2006) steps for conducting thematic analysis revealed four overarching themes that describe the participants’ experiences on TikTok during the lockdown. Prominent among the themes, the study uncovered how TikTok short videos excelled in impacting the research participants therapeutically; easing boredom, and impacting positively on their mental health. The present study suggests that TikTok short videos could be a phenomenon that could be adopted by individuals or even health professionals, especially psychotherapists in managing or treating patients in similar situations like the COVID-19 compulsory lockdown.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".