Bibliographic record
Abstract
The nursing shortage and high turnover rates are a problem in Canada and the world over. The purpose of this single case study was to explore leadership strategies that nurse leaders in specialty care clinics in Canada use to reduce nurse turnover. The participants were 7 nurse leaders from a single organization with specialty care clinics across Canada who all had above average nurse retention rates when compared to the case organization's average nurse retention rate. The authentic leadership theory was the conceptual framework. Data sources for this study were company documents, participants' semistructured interview responses, member checking of the interviews, and reflexive journal notes. Methodological triangulation was used to enhance validity. Data were analyzed using Yin's 5-step approach to qualitative data analysis. Data analysis yielded 4 categories of strategy themes for reducing nurse turnover: moral perspective, self-awareness, relational transparency, and balanced processing. The results of this study have the potential for positive social change in specialty care by providing senior leadership and nurse leaders of specialty care clinics with strategies that can contribute to nurse-retention initiatives. The availability of more nurses might improve the outcomes of patients who depend on these clinics for their regular infusion of specialty medicines to treat their critical illnesses, such as cancer or rare genetic diseases, where delay in treatment due to the unavailability of nurses can result in adverse consequences for patient care.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".