Experiences and Impacts of the COVID-19 Pandemic: A Thematic Analysis
Bibliographic record
Abstract
The COVID-19 pandemic prompted global public health restrictions that impacted Canadians in multiple ways. The effects of the pandemic are well examined in specific populations and in researcher-defined areas (e.g., mental health, physical activity, social connections, and financial impacts). Few studies explore the complex perspectives of adults who experienced and were impacted by the pandemic. The purpose of this study was to understand Canadian adults’ perspectives of pandemic impacts over time. Methods: A sample of 347 Canadian adults were recruited during the first six months of the COVID-19 pandemic to respond to open-ended questions about the pandemic’s impacts, administered every two weeks between April 2020 and January 2021. The responses were amalgamated into epochs, defined by dates that paralleled infection rates and public health responses in Canada. Qualitative thematic analysis identified major themes for each epoch and changes in themes over time. Results: The participants predominately reported adverse impacts of the pandemic during each epoch assessed, particularly with respect to mental health, future-oriented worry, activity restrictions, and social, and employment disruptions. Key concerns were potentially driven by changes in infection rates and public health policy changes. Conclusions: The COVID-19 pandemic impacted individuals in predominantly negative and complex ways that varied over time with public health responses. Findings from the present study may direct future pandemic responses to mitigate adverse effects to best prevent infection while preserving wellbeing.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".