Strategies for talent engagement and retention of Brazilian Nursing professionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To reflect on how human resource health managers and talent managers may engage and retain experienced nursing professionals in Brazil. METHODS: Reflection based on studies on global and Brazilian-specific nursing professionals and retention, before and during the COVID-19 pandemic. RESULTS: The pandemic worsened working conditions for all health professionals. Nursing professionals were particularly affected. Nurses have been viewed as "heroes" and "essential" frontline workers during the COVID-19 pandemic. However, despite the universal praise for their efforts, it seems uncertain if they were actually considered and managed like talent. FINAL CONSIDERATIONS: In order to develop a sustainable healthcare system supported by sufficient experienced nursing talent, healthcare human resource managers and talent managers must develop and implement impactful nursing talent retention and engagement strategies. We highlight possible strategies targeting experienced nursing talent that will help to sustain the Brazilian healthcare system, post-pandemic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it