Cross-sectoral video consultations in cancer care: perspectives of cancer patients, oncologists and general practitioners
Bibliographic record
Abstract
PURPOSE: Multidisciplinary video consultations are one method of improving coherence and coordination of care in cancer patients, but knowledge of user perspectives is lacking. Continuity of care is expected to have a significant impact on the quality of cancer care. Enhanced task clarification and shared responsibility between the patient, oncologist and general practitioner through video consultations might provide enhanced continuity in cancer care. METHOD: We used descriptive survey data from patients and doctors in the intervention group based on a randomised controlled trial to evaluate the user perspectives and fidelity of the intervention. RESULTS: Patients expressed that they were able to present their concerns in 95% of the consultations, and believed it was beneficial to have both their doctors present in 84%. The general practitioner and oncologist found that tripartite video consultation would lead to better coordination of care in almost 90% of the consultations. However, the benefits of handling social issues and comorbidity were sparser. Consultations were not accomplished in 11% due to technical problems and sound and video quality were non-satisfactory in 20%. CONCLUSION: Overall, multidisciplinary video consultations between cancer patient, general practitioner and oncologist were feasible in daily clinics. Initial barriers to address were technical issues and seamless planning. Patients reported high satisfaction, patient centredness and clarity of roles. General practitioners and oncologists were overall positive regarding role clarification and continuity, although less pronounced than patients. TRIAL REGISTRATION: www.clincialtrials.gov , NCT02716168.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".