Factors that influence the implementation of quality improvement programmes
Bibliographic record
Abstract
BACKGROUND: Access to poor-quality care has significant adverse effects on both morbidity and mortality. Therefore, it is essential to develop strategies that improve healthcare processes and systems, enhance patient satisfaction, and ensure access to high-quality care. In Haiti, the Ministry of Public Health and Population, donors, and technical and financial partners emphasize quality improvement programmes such as the HEALTHQUAL programme. It has become an essential approach in the Haitian health system. While quality improvement programs are relatively new to Haiti, little data are available on the factors that can facilitate or hinder their implementation. METHODS: An evaluative research (implementation analysis) using a multiple case study design was conducted with a qualitative approach. Thirty-eight semistructured interviews were conducted, observations were made, and documents were analysed. Data analysis was performed using the constant comparative method and a synthetic analysis framework with predefined categories (programme components, facilitating factors, inhibiting factors, mechanisms, consequences, and interactions between model elements). Subsequently, these categories were analysed using Atlas.ti software, with additional codes emerging and being incorporated into the predefined categories. Memos and a research journal were used during data collection and analysis. RESULTS: The main facilitating factors include collaboration between the institutional and community levels, leadership, coordination and collaboration within a multidisciplinary team, external pressures, the characteristics of the networks, team ownership of the process, and quality infrastructure. The main inhibiting factors comprise the sociopolitical context, organizational culture, the influence of previous decisions (policy legacy), provider perceptions of HEALTHQUAL, the lack of accountability mechanisms, an unsuitable health information system, and resource unavailability. CONCLUSION: Based on these observations, leaders and decision-makers are encouraged to consider these contextual factors when planning and implementing the HEALTHQUAL programme in Haiti. It is important to consider a range of contextual variables to understand implementation at any given time.
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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 | 0.024 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".