Managing ‘sick days’ in patients with chronic conditions: An exploration of patient and healthcare provider experiences
Bibliographic record
Abstract
INTRODUCTION: People with chronic medical conditions often take medications that improve long-term outcomes but which can be harmful during acute illness. Guidelines recommend that healthcare providers offer instructions to temporarily stop these medications when patients are sick (i.e., sick days). We describe the experiences of patients managing sick days and of healthcare providers providing sick day guidance to their patients. METHODS: We undertook a qualitative descriptive study. We purposively sampled patients and healthcare providers from across Canada. Adult patients were eligible if they took at least two medications for diabetes, heart disease, high blood pressure and/or kidney disease. Healthcare providers were eligible if they were practising in a community setting with at least 1 year of experience. Data were collected using virtual focus groups and individual phone interviews conducted in English. Team members analyzed transcripts using conventional content analysis. RESULTS: We interviewed 48 participants (20 patients and 28 healthcare providers). Most patients were between 50 and 64 years of age and identified their health status as 'good'. Most healthcare providers were between 45 and 54 years of age and the majority practised as pharmacists in urban areas. We identified three overarching themes that summarize the experiences of patients and healthcare providers, largely suggesting a broad spectrum in approaches to managing sick days: Individualized Communication, Tailored Sick Day Practices, and Variation in Knowledge of Sick Day Practices and Relevant Resources. CONCLUSION: It is important to understand the perspectives of both patients and healthcare providers with respect to the management of sick days. This understanding can be used to improve care and outcomes for people living with chronic conditions during sick days. PATIENT OR PUBLIC CONTRIBUTION: Two patient partners were involved from proposal development to the dissemination of our findings, including manuscript development. Both patient partners took part in team meetings and contributed to team decision-making. Patient partners also participated in data analysis by reviewing codes and theme development. Furthermore, patients living with various chronic conditions and healthcare providers participated in focus groups and individual interviews.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".