Strengthening healthcare delivery in <scp>H</scp>aiti through nursing continuing education
Bibliographic record
Abstract
AIM: The aim of this paper was to (1) highlight nursing continuing education as a key initiative for strengthening healthcare delivery in low-resource settings, and (2) provide an example of a nursing continuing education programme in Haiti. BACKGROUND: Haiti and other low-resource settings face extreme challenges including severe shortages of healthcare workers, high rates of nurse out-migration and variations in nurse competency at entry-to-practice. Nursing continuing education has the potential to address these challenges and improve healthcare delivery through enhanced nurse performance and retention; however, it is underutilized in low-resource settings. METHODS: A case study is presented from the Hôpital Universitaire de Mirebalais in Mirebalais, Haiti of a new nursing continuing education programme called the Beyond Expert Program. RESULTS: The case study highlights eight key dimensions of nursing continuing education in low-resource settings: (1) involving local stakeholders in planning process, (2) targeting programme to nurse participant level and area of care, (3) basing course content on local context, (4) including diverse range of nursing topics, (5) using participatory teaching methods, (6) addressing resource constraints in time and scheduling, (7) evaluating and monitoring outcomes, and (8) establishing partnerships. The case study provides guidance for others wishing to develop programmes in similar settings. CONCLUSION: Creating a nursing continuing education programme in a low-resource setting is possible when there is commitment and engagement for nursing continuing education at all levels of the organization. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Our report suggests a need for policy-makers in resource-limited settings to make greater investments in nursing continuing education as a focus of human resources for health, as it is an important strategy for promoting nurse retention, building the knowledge and skill of the existing nursing workforce, and raising the image of nursing in low-resource settings.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".