Capacity of nurses working in long‐term care: A systematic review qualitative synthesis
Bibliographic record
Abstract
BACKGROUND: The United Nations calculates there were 703 million adults 65 years and older globally as of 2019 with this number projected to double by 2050. A significant number of older adults live with comorbid health conditions, making the role of a nurse in long-term care (LTC) complex. Our objective was to identify the challenges, facilitators, workload, professional development and clinical environment issues that influence nurses and nursing students to seek work and continue to work in LTC settings. METHODS: Eligibility criteria included being a nurse in a LTC setting and research with a substantial qualitative component. Multiple databases (including Medline and CINAHL) were searched between 2013 and 2019 along with grey literature. Covidence was used to organise a team of 10 into a paired review of titles and abstracts to the final full text screening, extraction and appraisal with the CASP Qualitative Studies Checklist. Analysis involved a thematic synthesis approach. The Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) checklist informed the writing of the review. RESULTS: The search resulted in 18 articles and dissertations. Areas investigated included recruitment, resilience, employment and retention, how nurses perceived their professional work, rewards and difficulties, supervision, student preceptorship and career aspiration, nurses' perceptions of occupational status, along with leadership, education and development needs, and intentions to manage resident deteriorating health. The five themes were (1) perspectives of nursing influenced by the organisation, (2) pride in, and capacity to build relationships, (3) stretching beyond the technical skills, (4) autonomy, and (5) taking on the challenge of societal perceptions. DISCUSSION: This review revealed what is required to recruit nursing students to careers in LTC and retain nurses. To be explored is how staff can work to their full scope of practice and the resultant impact on resident care, including how to maximise a meaningful life for residents and their families. REGISTRATION: National Institute for Health Research UK (Prospero ID: CRD42019125214).
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".