Virtual health in cancer care: Results from a semi-structured interview-survey of oncology health care providers.
Bibliographic record
Abstract
e13618 Background: The COVID-19 pandemic has compelled an increased use of virtual care delivery models in oncology. This study sought to examine the views of oncology health care providers (HCP) in British Columbia on the value and impact of virtual care models in clinical practice. Methods: A semi-structured interview-survey was developed to compare provider practice patterns between May 2019 and May 2020. Questions were designed to determine provider-perceived value and impact of virtual visits on clinical interactions with patients. HCP (including physicians, dentists, and nurse practitioners) at BC Cancer were invited to participate. Responses to the interview questions were de-identified and HCP names were replaced with a study code. Quantitative questions were interpreted with descriptive statistics. Qualitative results were analyzed and iteratively coded by multiple reviewers for emerging themes. Results: Among 531 invited participants, 61 completed the interview-survey and 60 were included in the final analysis. Of those interviewed, 47% were radiation oncologists and 33% were medical oncologists. The remainder of HCP interviewed (n = 12) included functional imaging physicians, general practitioners in oncology, hereditary cancer physicians, nurse practitioners, palliative care physicians, psychiatrists, and surgical oncologists. Most oncology providers (87%) desired the continuation of virtual visits as part of their clinical practice so long as barriers to integration were addressed. Barriers identified included limited access to physical resources, such as hardware (70% responses) and quiet spaces (54% responses), insufficient logistic support such as information technology services (84% responses) and operational workflows (46% responses), the absence of guidelines to select patients for this delivery model (38% responses), and concerns regarding HCP liability, security and privacy (30% responses). Conclusions: Oncology HCP value delivering patient care through virtual means, however, barriers to implementation must be better understood. These data may inform continued use and implementation of virtual care at other oncology centers.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".