Giving a Voice to Nurse Managers and Staff Nurses: A Two-Centres Multi-Method Research Protocol to Optimize Nurses’ Actual Scope of Practice
Bibliographic record
Abstract
Introduction: Numerous studies have revealed that a limited time is devoted to value-added care activities that are part of nurses’ professional role (Déry et al., 2022). This has an impact on the performance of healthcare systems in terms of overall costs. The COVID-19 pandemic has put more pressure on healthcare organizations, on its nurse managers (NMs) and staff nurses. Objective: To mobilize key players in care units to propose effective and realistic strategies to facilitate the optimal deployment of nursing practice. Methods: This international research program encompasses 3 consecutive cross-sectional studies involving 2 healthcare centres: 1 in Canada and 1 in Switzerland. Study 1’s qualitative design will include focus groups with NMs. Study 2’s quantitative correlational design will survey staff nurses. Study 3 will include multiple meetings with NMs, staff nurses, clinical nurse specialists and educators (key players) to develop a logic model of intervention to propose effective and realistic strategies to facilitate the full deployment of nurses’ scope of practice. Discussion and Research Spin-offs: Study 1 will help describe the innovative management practices of NMs since the onset of the COVID-19 pandemic and identify their support and educational needs. Study 2 will explore the perceptions of staff nurses regarding the work psychological climate and work recognition, their actual scope of practice, their professional satisfaction at work and their perception of the quality of care. Study 3 will take advantage of this new data and mobilize key players in the identification of improvement strategies adapted to their own reality.
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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 | 0.036 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".