Exploring nursing expertise in residential care for older people: a mixed method study
Bibliographic record
Abstract
AIMS: To explore the expertise of Registered Nurses in residential care for older people. BACKGROUND: As older people in residential care have many complex dependencies, nursing expertise is an essential component of care excellence. However, the work of these nurses can be invisible and, therefore, unrecognized. Thus, additional attention is required to illuminate such nursing expertise. DESIGN: A mixed method design was used in this study. METHODS: The research took place in 2012 in the Republic of Ireland. Twenty-three case study nurses were recruited from nursing homes. Each case study nurse involved five data collection methods: shadowing, interview with a colleague, interview with a resident, a demographic profile and a director of nursing survey. The study was also informed by a modified focus group. Qualitative data were analysed using directed content analysis using a conceptual framework generated from the literature on nursing expertise. Quantitative data were analysed using SPSS and presented in descriptive statistics. FINDINGS: The findings from the case studies and the modified focus group are presented in seven themes, which represent nursing expertise in residential care of older people: transitions, context of the nursing home, saliency, holistic practice knowledge, knowing the resident, moral agency and skilled know how. CONCLUSION: Nursing expertise in residential care of older people is a complex phenomenon which encompasses many aspects of care delivery in a person-centred framework. By rendering this expertise visible, the need for appropriate and adequate skill mix for a growing residential care population is presented.
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 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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".