Nursing wound care practices in Haiti: facilitators and barriers to quality care
Bibliographic record
Abstract
AIM: To describe the facilitators and barriers for nurses to perform quality wound care in three surgical wards of a hospital in Port-au-Prince, Haiti. BACKGROUND: Up to a quarter of patients in low- and middle-income countries may acquire at least one infection while hospitalized. There is a paucity of research investigating nursing wound care practices in low- and middle-income countries regarding the prevention of hospital-acquired infections. METHODS: The design was qualitative descriptive. We observed nursing staff on the general surgery, orthopaedics and maternity units while they performed routine dressing changes (n = 15). We interviewed nursing (n = 13) and medical residents (n = 3) and inquired about their perceptions of facilitators and barriers for nurses to perform quality wound care. FINDINGS: A number of wound care practices appeared well integrated including using gloves to remove dressings, applying sterile dressings, properly disposing of soiled materials, inspecting wounds for signs of infection and employing comfort and privacy measures. Areas that may need improvement included aseptic technique, hand hygiene, pain assessments, patient education and documentation. We identified four themes related to barriers and facilitators to perform quality wound care: (i) materials and resources; (ii) nurse-to-patient ratios, workload and support; (iii) roles and responsibilities of nurses; and (iv) knowledge and training of nurses. CONCLUSION: Nursing wound care practices may be optimized by improving nurses' professional status and working conditions. IMPLICATIONS FOR NURSING PRACTICE AND HEALTH POLICY: Greater financial investment in health care and (continuing) education, self-regulation and development of the nursing role, including more autonomy, are needed to elevate the professional status of nurses in Haiti. Institutional policies should promote best practices, clarify nursing roles and responsibilities and foster interdisciplinary collaboration in patient care.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".