Moving the agenda forward: a person‐centred framework in long‐term care
Bibliographic record
Abstract
BACKGROUND: Internationally, the role of the registered nurse (RN) in long-term care (LTC) settings has evolved in response to the demands of governmental and organisational priorities. In stark contrast to the regulatory mandates, a person-centred care approach in LTC settings would require different outcomes, processes and competencies of the RN. AIMS: This article explores the implications of defining the RN's role in delivering person-centred care in LTC homes. METHODS: Based on a review of the literature, we present a framework that can be used to gather evidence on the outcomes, processes of care and competencies required of RNs to lead their teams to person-centred LTC homes. RESULTS: The development of the framework highlighted several issues: (i) current measures of quality in LTC settings focus on health outcomes and avoiding adverse events rather than on resident quality of life and well-being, which influences the RN's practice; (ii) person-centred care has emerged as a focus of care, yet measures currently developed are limited, and thus, new outcomes are proposed; (iii) to practice in a person-centred way, RNs must work through others on their team to ensure that staff truly relate to their residents, tailor approaches based on the remaining abilities of the residents and manipulate environments to match the competence of the individual, while focusing on residents' personhood and (iv) competencies of RNs to deliver person-centred care include leadership, facilitation, clinical excellence and critical thinking skills. CONCLUSIONS: RNs need to be supported, allowed and encouraged in redesigning their role, to work to their full capacity if they are truly to support person-centred care in LTC settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".