Clinicians’ Perceptions of the Benefits and Challenges of Teleoncology as Experienced Through the COVID-19 Pandemic: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: COVID-19 thrust both patients and clinicians to use telemedicine in place of traditional in-person visits. Prepandemic, limited research had examined clinician-patient communication in telemedicine visits. The shift to telemedicine in oncology, or teleoncology, has placed attention on how the technology can be utilized to provide care for patients with cancer. OBJECTIVE: Our objective was to describe oncology clinicians' experiences with teleoncology and to uncover its benefits and challenges during the first 10 months of the COVID-19 pandemic. METHODS: In-depth, semistructured qualitative interviews were conducted with oncology clinicians. Using an inductive, thematic approach, the most prevalent themes were identified. RESULTS: In total, 21 interviews with oncology clinicians revealed the following themes: benefits of teleoncology, such as (1) reducing patients' travel time and expenses, (2) limiting COVID-19 exposure, and (3) enabling clinicians to "see" a patients' lifestyle and environment, and challenges, such as (1) technological connection difficulties, (2) inability to physically examine patients, and (3) patients' frustration related to clinicians being late to teleoncology appointments. CONCLUSIONS: Teleoncology has many benefits and is well suited for specific types of appointments. Challenges could be addressed through improved communication when scheduling appointments to make patients aware about what to expect. Ensuring patients have the proper technology to participate in teleoncology and an understanding about how it functions are necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".