A Mixed Methods Assessment of Home-Based Video Pretransplant Lung Education
Bibliographic record
Abstract
Introduction: Patients awaiting lung transplantation must learn new information to successfully navigate the transplant process. A supplemental video series was piloted to patients at home during the Covid-19 pandemic to improve pre-transplant education. Methods: A mixed methods study was undertaken to assess patient experiences with this method of education, confirm the ideal timing of the education, and identify gaps that require further attention. Semi-structured interviews were conducted with 17 one-on-one or dyadic (patients and caregivers) who viewed the video series at home. A third-party researcher (not involved in creation of the educational materials) conducted the interviews by phone, which were audio recorded and then transcribed verbatim. NVivo 12 Pro for Windows software was used to code the data and identify emerging themes. Results: Participants indicated that home-based videos were applicable, and informative and helpful (4.7 on 5-point Likert scale) and appreciated the advice and experiences of real patients. They were satisfied with their transplant education (4.2/5). While there were few aspects that the participants disliked about the videos, the interviews elicited outstanding questions about the transplant process (eg, logistical aspects of travel) and transplant concerns (eg, medications, expenses, and precautions in daily life). Conclusion: Patients being assessed or listed for lung transplant valued the novel electronic video education, and we will implement the home-based process into standard of care after the patient's initial visit with the transplant respirologist. Pre-transplant education will be tailored to help address the outstanding gaps identified in this program evaluation.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".