Living with COVID-19 in the community during the first wave of the pandemic: Lessons from patients for healthcare providers and policy makers
Bibliographic record
Abstract
This qualitative descriptive study explores patients’ experiences of living with COVID-19, in the community, during the early stages of the pandemic. Between October 2020 and April 2021, fifteen semi-structured, video-recorded interviews were conducted, via Zoom, with participants in five Canadian provinces. Participants self-identified as having had a confirmed or suspected case of COVID-19. The constant comparative method was used to produce a thematic analysis of findings. Key findings include 1) PCR tests were not widely available in Canada, during the first wave, so many participants lacked a confirmed diagnosis and, subsequently, encountered challenges accessing specialist medical care; 2) Rapidly changing protocols around testing also impacted return to work as employers’ requirements were sometimes misaligned with public health guidelines; 3) Participants often found public health measures to be illogical, inconsistent, or sub-optimally implemented, and frequently perceived them as politically motivated rather than evidence-based; 4) some individuals with persistent symptoms had difficulty gaining acknowledgement and support for what is now more widely acknowledged to be long-COVID; and 5) The view that healthcare providers need a more nuanced approach to patients who lack a confirmed diagnosis or present with hard-to-explain symptoms was widely shared. There is the need for greater responsiveness to the lived experiences of patients with COVID-19, especially those with persistent symptoms, in developing clinical pathways and social supports. Experience Framework This article is associated with the Policy & Measurement lens of The Beryl Institute Experience Framework. (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".