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 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.020 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".