An Interpretive Description of Patient and Provider Perspectives on the Impact of the COVID‐19 Pandemic on Lung Transplant Care Access and Service Delivery in Alberta, Canada
Bibliographic record
Abstract
Background: The COVID‐19 pandemic impacted how health services were delivered for patients with chronic pulmonary conditions. To our knowledge, perceptions of patients with lung transplant (LT) and their providers on access to care and service delivery during the COVID‐19 pandemic have not been explored in our context of Alberta, Canada. Our objective was to explore LT patient and provider perspectives on the impact of the COVID‐19 pandemic on 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. Interviews were conducted virtually and confidentially transcribed verbatim. Data generation and analysis occurred concurrently. Analysis was informed by Braun and Clarke’s six phases of reflexive thematic analysis. Strategies to enhance rigor and trustworthiness of the findings were utilized. Results: We completed 17 interviews: 8 with patients and 9 with providers. Four key themes were generated: (1) “COVID‐19 created a relational wall;” (2) “Determining how care should be delivered was a juggling act;” (3) “Balancing supply and demand;” and (4) “The unique costs of being immunocompromised during a global pandemic.” The pandemic impacted social relationships for LT patients, especially through the use of virtual care. Several factors hindered access to care for LT patients. Provider participants highlighted how there were less transplants during the pandemic which created a backlog in transplant surgeries. Fear of COVID‐19 meant that some LT patients were hesitant to seek healthcare services, resulting in later‐term health consequences. A need for mental health services was identified among this population despite an apparent lack of available services. Participants highlighted the gap in COVID‐19 resources that now exists for this population since testing and treatments are no longer as readily available. Conclusions: In conclusion, provider participants did the best that they could with the circumstances they faced to provide high‐quality care to LT patients. However, while patient participants were generally understanding of circumstances, LT care suffered as a result of the COVID‐19 pandemic. Care for this population generally needs to be in‐person, but there is nuance surrounding this recommendation due to LT patients’ immunocompromised nature. Health system leaders can leverage our findings to implement learnings from the pandemic and continue to improve services for the ever‐growing LT population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".