How oncology teams can be patient‐centred? opportunities for theoretical improvement through an empirical examination
Bibliographic record
Abstract
BACKGROUND: In the context of interprofessional practice, a patient-centred approach is recommended, which generally means power-sharing, shared decision making and involving patients as part of the health-care team. These aspects, which are essential to "patient-centred" practice, do not appear to be sufficient to illustrate the full richness of this practice. OBJECTIVE: This article aimed to understand how interprofessional patient-centred (IPPC) practice in oncology teams contributes to creating a more positive experience for patients. Objectives were to (a) describe the IPPC practice of oncology teams using the IPPC Practice Framework; (b) determine the usefulness of this framework; and (c) offer alternative proposals for expanding our understanding of IPPC practice. DESIGN: A secondary analysis was performed with data from a multicase study designed to explore the effects of interdisciplinary work among oncology teams. Data were provided from six focus groups with professionals (n = 22) and patients diagnosed with cancer (n = 16). An iterative content analysis was performed. RESULTS: Applying the theoretical framework to data analysis enabled us to distinguish between the IPPC practice of the different teams and structure the data collected in order to show the processes and place them in context. However, it proved to be difficult to describe the central component of the theoretical framework, patient-centred processes. This situation raises new hypotheses for representing practice in a real-life context. An alternative perspective for illustrating IPPC practice is therefore proposed. CONCLUSION: This study emphasizes the importance of exploring the utility of theoretical frameworks and refining them in order to broaden our understanding of IPPC practice.
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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 | 0.064 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".