Enculturating, embedding and investing in fundamental care in an academic health science centre
Bibliographic record
Abstract
AIM: This manuscript aims to provide a discursive description of how one academic health care centre is enculturating, embedding and investing in the fundamental care framework and lessons learned that can serve as a blueprint for other organizations. BACKGROUND: A call to action to focus on fundamental care is not new as the initial Fundamentals of Care (FoC) Framework has been evolving over the last decade through efforts lead by the International Learning Collaborative (ILC). Now more than ever, there is a pressing need for leaders to influence a humane, compassionate evidence-informed approach to the COVID-19 pandemic and beyond by embedding an FoC framework and focusing on fundamental care as part of their academic mandate and daily care practices. DESIGN: This discursive paper delineates an evolving and ongoing enculturation, embeddedness and investment in advancing fundamental care as part of a larger academic practice strategy and quality improvement plan that is evidence-informed and collaborative in nature. METHOD: The action framework (value, talk, do, own and research fundamental care) developed by ILC guides efforts to how the FoC framework was embedded into one academic health science centre's strategic directions, academic practice strategy, professional practice model, quality plan and research and innovation platform. CONCLUSION: An overview of how we leveraged the FoC and ILC Leadership frameworks in our efforts to enculturate, embed and invest in advancing fundamental care and lessons learned that may inform other healthcare organizations in their efforts. IMPACT ON NURSING SCIENCE, PRACTICE OR DISCIPLINARY KNOWLEDGE: Underpinning all of our efforts is the integral value we place on fundamental care to guide how we practice, educate and learn, discover and innovate and lead at x. We shared how we value, talk, do, own and research fundamental care by having it embedded into our strategic directions, academic practice strategy, professional practice model, quality aims and research and innovation platform. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".