Embodying person‐centred being and doing: Leading towards person‐centred care in nursing homes as narrated by managers
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore how managers describe leading towards person-centred care in Swedish nursing homes. BACKGROUND: Although a growing body of research knowledge exists highlighting the importance of leadership to promote person-centred care, studies focused on nursing home managers' own descriptions of leading their staff towards providing person-centred care is lacking. DESIGN: Descriptive interview study. COREQ guidelines have been applied. METHODS: The study consisted of semi-structured interviews with 12 nursing home managers within 11 highly person-centred nursing homes purposively selected from a nationwide survey of nursing homes in Sweden. Data collection was performed in April 2017, and the data were analysed using content analysis. RESULTS: Leading towards person-centred care involved a main category; embodying person-centred being and doing, with four related categories: operationalising person-centred objectives; promoting a person-centred atmosphere; maximising person-centred team potential; and optimising person-centred support structures. CONCLUSIONS: The findings revealed that leading towards person-centred care was described as having a personal understanding of the PCC concept and how to translate it into practice, and maximising the potential of and providing support to care staff, within a trustful and innovative work place. The findings also describe how managers co-ordinate several aspects of care simultaneously, such as facilitating, evaluating and refining the translation of person-centred philosophy into synchronised care actions. RELEVANCE TO CLINICAL PRACTICE: The findings can be used to inspire nursing home leaders' practices and may serve as a framework for implementing person-centred care within facilities. A reasonable implication of these findings is that if organisations are committed to person-centred care provision, care may need to be organised in a way that enables managers to be present on the units, to enact these strategies and lead person-centred care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".