Telehealth outpatient palliative care in the COVID-19 pandemic: patient experience qualitative study
Bibliographic record
Abstract
OBJECTIVES: Outpatient in-person early palliative care improves quality of life for patients with advanced cancer. The COVID-19 pandemic forced a rapid shift to telehealth visits; however, little is known about how telehealth in outpatient palliative care settings should be optimised beyond the pandemic. We aimed to explore, from the perspective of patients attending an outpatient palliative care clinic, the most appropriate model of care for in-person versus telehealth visits. METHODS: A qualitative study using the grounded theory method. One-on-one, semistructured qualitative interviews were conducted with 26 patients attending an outpatient palliative care clinic at a tertiary cancer centre recruited from two groups: (1) those with >1 in-person appointment prior to 1 March 2020 and >1 telehealth appointment after this date (n=17); and (2) patients who had exclusively telehealth appointments (n=9). Purposive sampling was used to incorporate diverse perspectives. RESULTS: Overall, participants endorsed a flexible hybrid approach incorporating both in-person and telehealth visits. Specific categories were: (1) in-person outpatient palliative care supported building interpersonal connections and trust; (2) telehealth palliative care facilitated greater efficiency, comfort and independence and (3) patient-preferred circumstances for in-person visits (preferred for initial consultations, visits where a physical examination may be required and advance care planning discussions), versus telehealth visits (preferred during periods of relative heath stability). CONCLUSIONS: The elements of in-person and telehealth outpatient palliative care clinic visits described by patients as integral to their care may be used to develop models of hybrid outpatient palliative care delivery beyond the pandemic alongside reimbursement and regulatory guidelines.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".