Exploring the impacts of COVID-19 before lung transplantation: A qualitative study
Bibliographic record
Abstract
RATIONALE: Patients being assessed or listed for a lung transplant face significant challenges, and the impact of the COVID-19 pandemic on this population is unknown.OBJECTIVES A qualitative study was undertaken to explore the impacts of COVID-19 within this context.METHODS Patients who were being assessed or waiting for a lung transplant (n = 22) and their caregivers (n = 3) participated in semi-structured interviews conducted by phone. Interviews were transcribed and imported into NVivo 12 Pro software for the exploration and coding of data to arrive at themes.MAIN RESULTS: The majority of patients (n = 16) described limitations in their access to healthcare, including difficulty seeing their physician, delayed appointments and decreased in-person visits. Physical activity was greatly impacted (n = 15) “diminished,” “restricted” or “limited” or impacted “dramatically” due to COVID-19. Significant limitations were imposed on physical activities (n = 15) and social activities (n = 18), and financial concerns (n = 11) were common amongst this population. For many, these impacts contributed to mental health struggles including increased stress, anxiety and depression. Patients coped using a variety of strategies, including partaking in hobbies, meditation and spirituality, mindset and self-talk, and expressed gratitude for their current situation and hope for the future.CONCLUSION Patients being assessed or listed for lung transplant have been affected by the global pandemic driven by an illness that is respiratory in nature. Patients who continue to struggle should be identified and appropriate supports should be provided. In the event of a future pandemic, the vulnerabilities in this population should be considered.
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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 | 0.013 | 0.017 |
| 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 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".