Construing Worst Experiences of the COVID-19 Pandemic in the USA: A Thematic Analysis
Bibliographic record
Abstract
The COVID-19 pandemic has not only resulted in millions of deaths but, together with the strategies imposed to contain the spread of the disease, it has had significant psychological and social effects. This paper considers these effects in residents of the USA, the country that has reported the highest number of deaths from COVID-19. Between April and May, 2020, responses were obtained to an on-line survey, which included asking participants, recruited by snowball sampling, to describe their worst experience of the pandemic. The responses of 741 participants, primarily female and Caucasian, were subjected to a thematic content analysis which used a primarily deductive approach in which these responses were viewed in terms of transitions in construing. The transition themes identified were anxiety; threat; loss of role; sadness; contempt; and stress. Various subthemes were also identified. The study provided further evidence of the utility of a personal construct framework in conceptualizing experiences associated with illness and the risk of this. Implications of its findings are considered at both an individual and a societal level.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".