Bibliographic record
Abstract
This content analysis examined 653 Twitter tweets from two threads in order to explore the ways in which emotional concerns are contextualized during the COVID-19 pandemic and sought to identify coping mechanisms mentioned in tweets following government-legislated lockdowns and social isolation measures. A purposive sampling method was employed to collect tweets possessing characteristics of interest to the present study. An open-coding procedure was utilized to examine any salient meanings or keywords, and the frequency of occurrence of contextualized emotional concerns and identified coping mechanisms was recorded. Results revealed 7 main ways within which emotional concerns were framed, including: COVID-19 Virus, School-Related, Groups/Individuals, Social Institutions, Financial/Work-Related, Mass Media, and Other. Results also revealed 10 themes in which coping mechanisms were identified: Hobbies/Interests, Social Media, Offering Resources, Substance Use, Connecting with Others, Eating, Raising Awareness/Promoting Compliance, Religion/Optimism, Humor/Sarcasm, and Other. Although previous literature has demonstrated that people exhibit psychological distress during a global health crisis, this study adds to the growing body of literature on COVID-19 and outlines the contexts in which emotional concerns arise during a pandemic and how people are coping through these unprecedented times. These findings provide insight into how individuals are sharing concerns about their mental health with others via Twitter during the COVID-19 pandemic, and points to the need for psychological interventions specifically oriented towards global health crises in the midst of government mandated lockdown measures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".