How Do Charge Nurses View Their Roles in Long-Term Care?
Bibliographic record
Abstract
This article explores how registered nurses (RNs) in long-term care (LTC) understand their role as charge nurses. Data are derived from 16 charge nurses employed in 8 facilities in Ontario, Canada. Qualitative methods are used to analyze audiotapings of interviews. The findings reveal a range of dimensions and subdimensions. Charge nurses experience their work as highly complex and unpredictable. Themes that captured the following dimensions of the supervisor role in LTC include (a) against all odds, getting through the day; (b) stepping in work; and (c) leading and supporting unregulated care workers. In addition, analysis within each category reveals a complex intersection between the nurses’ perceptions of the context and their consequent work strategies. The emerging demands placed on supervisors due to the growing complexity of residents, increasing government regulations, and staffing shortages have caused the role of the charge nurse to evolve with little reflection on its impact.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".