Cross-sectoral communication by bringing together patient with cancer, general practitioner and oncologist in a video-based consultation: a qualitative study of oncologists’ and nurse specialists’ perspectives
Bibliographic record
Abstract
Shared care models in the field of cancer aim to improve care coordination, role clarification and patient satisfaction. Cross-sectoral communication is pivotal. Involvement of patients may add to intended mechanisms.A randomised controlled trial 'The Partnership Study' tested the effect of bringing together patient, general practitioner (GP) and oncologist for a consultation conducted by video. PURPOSE: As part of the process evaluation, this study aimed to explore experiences, attitudes and perspectives of the oncological department on sharing patient consultations with GPs using video. METHODS: Four semistructured interviews with five oncologists and four nurse specialists were conducted in February 2020. We focused on the informants' experiences and reflections on the potential of future implementation of the concept 'inviting the GP for a shared consultation by video'. The analyses were based on an inductive, open-minded, hermeneutic phenomenological approach. RESULTS: A total of six overall themes were identified: structuring consultation and communication, perceptions of GP involvement in cancer care, stressors, making a difference, alternative ways of cross-sector communication and needs for redesigning the model. The concept made sense and was deemed useful, but solving the many technical and organisational problems is pivotal. Case-specific tasks and relational issues were targeted by pragmatically rethinking protocol expectations and the usual way of communication and structuring patient encounters. Case selection was discussed as one way of maturing the concept. CONCLUSION: This Danish study adds new insight into understanding different aspects of the process, causal mechanisms as well as the potential of future implementation of video-based tripartite encounters. Beyond solving the technical problems, case selection and organisational issues are important. Acknowledging the disruption of the usual workflow, the introduction of new phases of the usual encounter and the variety of patient-GP relationships to be embraced may help to better understand and comply with barriers and facilitators of communication and sharing. TRIAL REGISTRATION NUMBER: 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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".