Applying the principles of adaptive leadership to person‐centred care for people with complex care needs: Considerations for care providers, patients, caregivers and organizations
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Health systems in many countries see person-centred care as a critical component of high-quality care but many struggle to operationalize it in practice. We argue that models such as adaptive leadership can be a critical lever to support person-centred care, particularly for people who have multiple complex care needs. OBJECTIVE: To reflect on two concepts: person-centred care and adaptive leadership and share how adaptive leadership can advance person-centred care at the front-line care delivery level and the organizational level. FINDINGS: The defining feature of adaptive leadership is the separation of technical solutions (ie applying existing knowledge and techniques to problems) from adaptive solutions (ie requiring shifts in how people work together, not just what they do). Addressing adaptive challenges requires identifying key assumptions that may limit motivations for change and the behaviours influenced by these assumptions. Thus, effective care for patients, particularly those with multiple complex care needs, often entails helping care providers and patients to examine their relationships and behaviours not just identifying technical solutions. Addressing adaptive challenges also requires a supportive and enabling organizational context. We provide illustrative examples of how adaptive leadership principles can be applied at both the front line of care and the organization level in advancing person-centred care delivery. CONCLUSIONS: Advancing person-centred care at both the clinical and organizational levels requires a growth mindset, a willingness to try (and fail) and try again, comfort in being uncomfortable and a commitment to figure things out, in partnership, in iterative ways. Patients, caregivers, care providers and organizational leaders all need to be adaptive leaders in this endeavour.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it