Seeking Care for Long COVID: A Narrative Analysis of Canadian Experiences
Bibliographic record
Abstract
The goal of this study was to explore the experiences of individuals seeking care for long COVID-19 in the Canadian healthcare system. Recorded virtual interviews were carried out with 8 participants and narrative analysis was used to examine the stories produced and identify the central narratives that defined participants' experiences. Care-seeking experiences were characterized by (1) often debilitating multi-system symptoms for which little information about prognosis was available and no effective treatments were provided, (2) compounded by the frustration of trying to convince family, friends, and health care practitioners of the legitimacy of their illness, (3) access to medical care was severely limited by the global pandemic and associated higher thresholds for care, (4) like others suffering from complex, multi-system conditions, people with long COVID are often struggling with a health-care system ill-suited for dealing with long-term and possibly chronic conditions. To make system-level improvements to better serve those with chronic conditions, it is critical that we understand the care-seeking experiences of chronic illness patients, including the unique experiences of those with long COVID.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".