A longitudinal mixed methods examination of stress during the COVID-19 pandemic in a Canadian sample
Bibliographic record
Abstract
Background: Stress is a universal experience, which has been exacerbated for many during the COVID-19 pandemic. The overarching goal of this work was to examine the experiences of stress among Canadians over a one-year period during the COVID-19 pandemic. Within this, I aimed to qualitatively understand the greatest stressors Canadians were experiencing at each time point and contextualize their experiences longitudinally. I also aimed to quantitatively understand the prevalence of stress at each time point and over time. Lastly, I used a mixed methods approach to gain a rich understanding of the main stressors qualitatively identified by participants across all time points. Methods: The COVID Survey Canada data were collected between May 2020 and July 2021. Participants (N = 1,074) were recruited via social media platforms and were invited to complete an online baseline survey and two follow-up surveys at six months (n = 484) and one-year (n = 406) following their initial survey completion. I used an exploratory sequential mixed methods approach for data analysis, where I first analyzed the open-ended responses to, “what are you most stressed/concerned about right now?” using reflexive thematic analysis (Braun & Clarke, 2006; 2019; 2023 for three time points individually, and then completed a qualitative longitudinal analysis using interpretative phenomenological analysis (Smith & Osborn, 2007). I quantitatively analyzed the prevalence of both perceived stress and COVID stress at each time point. Guided by the qualitative longitudinal framework, I chose several variables that mapped on to the qualitative longitudinal framework (COVID impact, income change, job loss, and social support) and completed descriptive analyses to provide the prevalence for each variable at all time points. Results: Participants qualitatively identified many stressors at each time point, and five main themes were identified in the longitudinal qualitative framework: the impact of COVID-19, health and wellbeing, economic instability, social connection, and pandemic related guidelines and restrictions. Quantitative analyses supported qualitative findings and demonstrated high rates of perceived stress across each time points, with the highest level of perceived stress at time 1 (76.4%, 71.5%, and 71.5%, respectively). Discussion: These findings highlight the difficult experiences many Canadians went through during COVID-19 and can be used to inform policies, supports, and interventions for both current Canadians experiencing chronic stress due to COVID as well as for future pandemic supports and interventions.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".