Patient and family centered nursing rounds as a platform for continuing education of nurses in a rural hospital in Haiti
Bibliographic record
Abstract
Introduction: Haiti has one of the most severe health care worker shortages in the Americas. In-service continuing education opportunities have been linked to increased nurse motivation and retention, and improved patient outcomes. This paper describes how an academic-non-profit collaboration adapted nursing rounds to create a bedside teaching activity for nurses at a Haitian hospital.Methods: Rounds are defined as a gathering of nursing staff and students, as well as the patient’s family at the patient’s bedside, for case presentation and discussion about the medical and nursing care plans. A survey of participants was completed on a quarterly basis to improve the activity and assess whether the project was meeting its goals.Results: Twenty-six nurses participated in the first quarter survey and twenty-five in the second quarter survey. Surveys showed that participation in rounds increased over time. Nurses were either satisfied or very satisfied with rounds. The majority of nurses reported learning information that improved their patient care every time they attended rounds. Challenges included limited staffing at the hospital, nurses’ varying levels of literacy, and Haiti’s unpredictable political climate. These were overcome by building a partnership with a reputable local organization, accompanying local colleagues in a peer-to-peer model, and embracing incremental changes during implementation.Conclusions: Evidence from observation, informal feedback, and responses to participant surveys indicates that rounds may increase opportunities for continuing education, encourage patient and family centered care, and promote inter-professional collaboration. This project has proved to be sustainable and continues to evolve two years following implementation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".