What is best for Esther? A simple question that moves mindsets and improves care
Bibliographic record
Abstract
BACKGROUND: Persons in need of services from different care providers in the health and welfare system often struggle when navigating between them. Connecting and coordinating different health and welfare providers is a common challenge for all involved. This study presents a long-term regional empirical example from Sweden-ESTHER, which has lasted for more than two decades-to show how some of those challenges could be met. The purpose of the study was to increase the understanding of how several care providers together could succeed in improving care by transforming a concept into daily practice, thus contributing with practical implications for other health and welfare contexts. METHODS: The study is a retrospective longitudinal case study with a qualitative mixed-methods approach. Individual interviews and focus groups were performed with staff members and persons in need of care, and document analyses were conducted. The data covers experiences from 1995 to 2020, analyzed using an open inductive thematic analysis. RESULTS: This study shows how co-production and person-centeredness could improve care for persons with multiple care needs involving more than one care provider through a well-established Quality Improvement strategy. Perseverance from a project to a mindset was shaped by promoting systems thinking in daily work and embracing the psychology of change during multidisciplinary, boundary-spanning improvement dialogues. Important areas were Incentives, Work in practice, and Integration, expressed through trust in frontline staff, simple rules, and continuous support from senior managers. A continuous learning approach including the development of local improvement coaches and co-production of care consolidated the integration in daily work. CONCLUSIONS: The development was facilitated by a simple question: "What is best for Esther?" This question unified people, flattened the hierarchy, and reminded all care providers why they needed to improve together. Continuously focusing on and co-producing with the person in need of care strengthened the concept. Important was engaging the people who know the most-frontline staff and persons in need of care-in combination with permissive leadership and embracing quality improvement dimensions. Those insights can be useful in other health and welfare settings wanting to improve care involving several care providers.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".