Investigating whether shared video-based consultations with patients, oncologists, and GPs can benefit patient-centred cancer care: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Guidelines have proposed that GPs should have a central role as coordinators of care and support patients with cancer during all stages of treatment, follow-up, and rehabilitation. Multidisciplinary video consultation involving the patient with cancer, the oncologist, and the GP may help to define roles and tasks, and this resulting clarity may enable greater support for patients with cancer. AIM: To explore the consultation structure, content, and task clarification when a GP and an oncologist are attending a video consultation with a patient with cancer. DESIGN & SETTING: A qualitative study took place in the Region of Southern Denmark to investigate multidisciplinary video consultations, based on thematic analysis. METHOD: Recordings of 12 video consultations were analysed using the framework method. A combined deductive and inductive approach was undertaken. The deductive themes were selected based on a consultation guide given to the doctors before the consultations. RESULTS: The study identified 15 themes, which were grouped into the following three categories: the implications of sharing a consultation; consultation structure; and health concerns. CONCLUSION: Multidisciplinary video-based consultations with a patient and two health professionals succeeded in having a patient-centred communication style. In clarifying tasks between the GP and oncologist to support the patient, work-related issues and professional support for psychosocial challenges were always a task for the GP. Dissemination of this first-line evidence may improve acceptability among medical specialists and help assist GPs in supporting patients with cancer. However, focus on the involvement of relatives should be emphasised.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| 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".