COVID-19-Induced Anxiety and Covid-19 Precautionary Measures as Predictors of Mental Wellbeing of Nigerians
Bibliographic record
Abstract
Abstract Introduction: The present study investigated the COVID-19-induced anxiety and Covid-19 precautionary measures as predictors of mental wellbeing of Nigerians during the COVID-19 pandemic. Method: By using a cross-sectional design methodology, 340 participants (58.2% males and 41.8% females) were selected through the snowball sampling technique. Their age ranged from 15 to 56 years (M=26.66; SD=6.485). Participants responded to the COVID-19-Induced Anxiety Questionnaire (C-19-IAQ), COVID-19 Precautionary Measures Questionnaire (C-19PMQ), and the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS). Results: Standard multiple regression analyses indicated that COVID-19 induced-anxiety and COVID-19 precautionary measures significantly predicted mental wellbeing [R= .082; R2 -.077;F (2,337) =15.096, P < .05]. However, only COVID-19 induced-anxiety [b= -.25, t (339)=-3.94,p < .05] independently predicted mental wellbeing. Conclusion: Based on these findings, the researchers recommended that mental wellbeing in the era of COVID-19 should be prioritized and given necessary attention by governments and other stakeholders. Information on COVID-19 should be structured in a manner that promotes health knowledge of the pandemic rather than inducing fear and anxiety. Individuals are also urged to engage in anxiety-reducing techniques which may help curb the rise of anxiety in these times of the COVID-19.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".