Engaging with Diversity and Complexity using Collaborative Approaches to Decision Making
Bibliographic record
Abstract
A key challenge in contemporary dietetic practice is making collaborative decisions about dietary behaviours with a diverse range of patients. Contemporary decision making frameworks for clinical dietetic practice give value to working in a collaborative manner with patients, however, there remains uncertainty with regards to how and when dietitians might apply this approach in their practice.In this doctoral research project, Author 1 used a philosophical hermeneutic approach to deepen understanding of a collaborative approach to decision making in dietetic practice. She also explored the core capabilities required to enact such an approach in early career dietetic practice. The experiences and perceptions of patients and dietitians were explored using in depth interviews and individualized reflective practice activities.The findings suggest that collaborative decision making in dietetic practice is situational and requires the development of a caring and trusting professional relationship to be effective. Other core capabilities needed to enact this approach relate to developing self awareness, establishing an open and transparent dialogue, identifying and exploring common ground and finding the time to think and talk.The final product of the research, the Interpretive Engagement Model of Collaborative Decision Making (Author 1, 2013), can be used as a framework to help practitioners to reflect on their decision making practice.Early exposure in tertiary education to critical dialogues and questioning current practices will cultivate early career dietitians’ capabilities to develop their collaborative decision making practice in future.
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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 | 0.049 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".