Living with cystic fibrosis during the COVID-19 pandemic: An interpretive description of healthcare access from patients with cystic fibrosis and their providers in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: The current study aimed to explore patient and provider perspectives of the impact of the pandemic on cystic fibrosis healthcare access and service delivery. METHODS: We used Interpretive Description, a qualitative approach with the end-goal of informing decisions and actions in clinical practice by generating findings that are clinically meaningful and useful. Levesque et al.'s "Conceptual framework of access to health care" informed the development of our interview guides. Interviews were conducted via telephone or Zoom and confidentially transcribed verbatim. Data generation and analysis occurred concurrently to allow for iterative refinements of the interview guides. Analysis was informed by Braun and Clarke's six phases of reflexive thematic analysis. Strategies to enhance rigour and trustworthiness of the findings were utilized. RESULTS: We completed 13 interviews: 8 with patients and 5 with providers. Three key themes were generated: (a) Tensions due to infection prevention at micro- meso-, and macro- levels; (b) Modifying aspects of person-focused care can bolster perceived quality of clinical encounters; and (c) Accessibility of appropriate healthcare services could improve efficiency of service delivery. Infection prevention at the individual level was not found to be burdensome. Society's compliance with public health measures, or lack thereof, impacted the level of stigma and anxiety experienced by patients with cystic fibrosis. A changed model of care reliant on patient self-report instead of clinician-led testing and in-person assessment due to the transition to virtual care was associated with mixed perceptions since patients with cystic fibrosis were comfortable making care decisions but many participants (patient and provider) felt challenged by the lack of objective data for decision-making. It was essential for patients with cystic fibrosis to feel known, heard, and seen by their providers in order to feel the care was effective. Finally, critical insights around the need for a balance of in-person and virtual care as well as the need for mental health supports were offered. CONCLUSIONS: The learnings from this study could be translated into practical strategies for improving cystic fibrosis care during the pandemic and beyond. We recommend: (1) a hybrid approach to care moving forward, (2) each patient having a lead physician with others filling in as necessary when scheduling demands, and (3) a reallocation of resources to fund a mental health practitioner position.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".