Redefining clinical leadership for team-course development
Bibliographic record
Abstract
Objective: Value choices are rooted in the philosophical deliberations of Aristotle, Levinas and Gadamer. Balancing the needs of “the other” with societal and institutional needs to meet the objectives of “the cause” is core to the modern health systems priority setting debate. These value conflicts present themselves bed-side in the day-to-day decision-making processes. A clinical leadership (CL) framework should present solutions to this challenge.Methods: The definition of CL is redefined to include four key values involved in this value conflict. These are 1) trust, 2) quality, 3) responsiveness and 4) efficiency. A CL in Teams course curriculum and design was developed to link these values to tools and to context in the hospital.Results: A new definition of CL has provided a common formative framework useful in clinical settings for priority setting, evaluation and professional development. By the end of 2015 a total of 82 participants will have completed the course. It has been evaluated to be timely, feasible, flexible, relevant and sustainable.Conclusions: Values influence how clinical leaders operate and have an important impact on their leadership abilities and how they respond to challenges. For clinical leaders and teams to work effectively it is crucial to develop common basic values. Developing a set of tools and reflective practice to understand the inter-relationship between values and how they can conflict or reinforce each other, contributes to improved quality of service, patient-centred care and workforce satisfaction.
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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 | 0.048 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".