Going home positive: a qualitative study of the experiences of care for patients with COVID-19 who are not hospitalized
Bibliographic record
Abstract
BACKGROUND: Most Canadians diagnosed with COVID-19 have had mild symptoms not requiring hospitalization. We sought to understand the patient experience of care while being isolated at home after testing positive for SARS-CoV-2 infection. METHODS: We conducted a phenomenologically informed qualitative descriptive study using in-depth semistructured interviews to identify common themes of experience for patients sent home from hospital with a positive COVID-19 diagnosis. Between July and December 2020, we conducted interviews with patients who were followed by the North York General Hospital COVID Follow-Up Clinic. Patients with mild to moderate symptoms were interviewed 4 weeks after their COVID-19 diagnosis. We conducted the interviews and performed a thematic analysis of the data concurrently, in keeping with the iterative process of qualitative methodology. RESULTS: We conducted interviews with 26 patients. From our analysis, 3 themes were developed regarding participants' overall experience: lack of adequate communication, inconsistency of information from various sources, and the social implications of a COVID-19 diagnosis. The implications of a positive test for SARS-CoV-2 infection are substantial, even when symptoms are mild and patients self-isolate as recommended. Participants noted communication challenges and inconsistent information, leading to exacerbated stress. INTERPRETATION: Participants shared their experiences of the stigma of testing positive and the frustration of poor communication structures and inconsistent information. Experiencing care during self-isolation at home is an area of increasing importance, and these findings can inform improved support, ensuring access to equitable and safe COVID-19 care for these patients.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".