Implementing the Emergency Triage, Assessment and Treatment plus admission care (ETAT+) clinical practice guidelines to improve quality of hospital care in Rwandan district hospitals: healthcare workers’ perspectives on relevance and challenges
Bibliographic record
Abstract
BACKGROUND: An emergency triage, assessment and treatment plus admission care (ETAT+) intervention was implemented in Rwandan district hospitals to improve hospital care for severely ill infants and children. Many interventions are rarely implemented with perfect fidelity under real-world conditions. Thus, evaluations of the real-world experiences of implementing ETAT+ are important in terms of identifying potential barriers to successful implementation. This study explored the perspectives of Rwandan healthcare workers (HCWs) on the relevance of ETAT+ and documented potential barriers to its successful implementation. METHODS: HCWs enrolled in the ETAT+ training were asked, immediately after the training, their perspective regarding (i) relevance of the ETAT+ training to Rwandan district hospitals; (ii) if attending the training would bring about change in their work; and (iii) challenges that they encountered during the training, as well as those they anticipated to hamper their ability to translate the knowledge and skills learned in the ETAT+ training into practice in order to improve care for severely ill infants and children in their hospitals. They wrote their perspectives in French, Kinyarwanda, or English and sometimes a mixture of all these languages that are official in the post-genocide Rwanda. Their notes were translated to (if not already in) English and transcribed, and transcripts were analyzed using thematic content analysis. RESULTS: One hundred seventy-one HCWs were included in our analysis. Nearly all these HCWs stated that the training was highly relevant to the district hospitals and that it aligned with their work expectation. However, some midwives believed that the "neonatal resuscitation and feeding" components of the training were more relevant to them than other components. Many HCWs anticipated to change practice by initiating a triage system in their hospital and by using job aids including guidelines for prescription and feeding. Most of the challenges stemmed from the mode of the ETAT+ training delivery (e.g., language barriers, intense training schedule); while others were more related to uptake of guidelines in the district hospitals (e.g., staff turnover, reluctance to change, limited resources, conflicting protocols). CONCLUSION: This study highlights potential challenges to successful implementation of the ETAT+ clinical practice guidelines in order to improve quality of hospital care in Rwandan district hospitals. Understanding these challenges, especially from HCWs perspective, can guide efforts to improve uptake of clinical practice guidelines including ETAT+ in Rwanda.
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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 | 0.018 | 0.025 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".