Unpacking the multiple dimensions and levels of responsibility of the charge nurse role in long‐term care facilities
Bibliographic record
Abstract
AIM: The charge nurse in long-term care facilities (LTCFs) performs a multiplicity of tasks that range from oversight of the entire facility to directly assisting residents in activities of daily living. In order to refine resident-centred care strategies and to increase the quality of care provided in LTCFs, this study aims at gaining a more nuanced understanding of the different dimensions of the charge nurse role as a central figure in these institutions. METHODS: Data were generated through semi-structured interviews. A purposive sample of ten Registered Nurses in a charge nurse role, diverse in experience, age, gender and background, was recruited from five LTCFs in Ontario, Canada. The study used a combination of conventional and direct qualitative content analyses. FINDINGS: All tasks performed by the charge nurses were categorised in four dimensions: clinical, supervisory, team support and managerial. Administration was a cross-cutting sub-dimension which has gained presence over the years. Depending on the shift worked and the organisational structure of the facility, each dimension gained or lost weight as part of the overall role. CONCLUSION: These findings suggest that the charge nurse role is in a state of flux and lacking standardisation within and across facilities. LTCFs would benefit from increasing recognition of the role according to the wide range of tasks performed and responsibilities assumed, and by recruiting their charge nurses accordingly. IMPLICATIONS FOR PRACTICE: The proposed conceptual framework could be used to map and assess charge nurses' workloads and responsibilities, in order to enhance staff satisfaction and resident-centred care in LTCFs.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".