The Health Narratives Research Group (HeNReG): A self-direction process offered to help decrease burnout in public health nurse practitioners
Bibliographic record
Abstract
century sex roles and public health concerns, nursing evolved as other-directed, dependent on physician-focused diagnosis, prescription decisions, and public health advancements. The result of this other direction is that public health nurse practitioners have endured significant workplace stress resulting in burnout, especially during COVID-19. To help decrease their burnout, nurses require development of self-direction. The Health Narratives Research Group (HeNReG) has the potential to reduce burnout in nurse practitioners by encouraging the development of self-direction. The HeNReG process is presented through historically analyzed documents regarding reducing burnout in health researchers by developing self-direction including: (1) three years of archived year-end feedback results provided by participants, (2) archived participant responses to specific HeNReG-related writing prompts, and (3) a comparison of HeNReG results with the outcomes of resilience programs. The conclusion-the HeNReG offers an effective option for reducing burnout in health researchers that has the potential to decrease nurse practitioner burnout in a way that resilience programs do not. Tailoring the HeNReG process to public health nurses is discussed, inviting future research for reducing burnout in public health nurses.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".