The impact of accreditation of primary healthcare centers: successes, challenges and policy implications as perceived by healthcare providers and directors in Lebanon
Bibliographic record
Abstract
BACKGROUND: In 2009, the Lebanese Ministry of Public Health (MOPH) launched the Primary Healthcare (PHC) accreditation program to improve quality across the continuum of care. The MOPH, with the support of Accreditation Canada, conducted the accreditation survey in 25 PHC centers in 2012. This paper aims to gain a better understanding of the impact of accreditation on quality of care as perceived by PHC staff members and directors; how accreditation affected staff and patient satisfaction; key enablers, challenges and strategies to improve implementation of accreditation in PHC. METHODS: The study was conducted in 25 PHC centers using a cross-sectional mixed methods approach; all staff members were surveyed using a self-administered questionnaire whereas semi-structured interviews were conducted with directors. RESULTS: The scales measuring Management and Leadership had the highest mean score followed by Accreditation Impact, Human Resource Utilization, and Customer Satisfaction. Regression analysis showed that Strategic Quality Planning, Customer Satisfaction and Staff Involvement were associated with a perception of higher Quality Results. Directors emphasized the benefits of accreditation with regards to documentation, reinforcement of quality standards, strengthened relationships between PHC centers and multiple stakeholders and improved staff and patient satisfaction. Challenges encountered included limited financial resources, poor infrastructure, and staff shortages. CONCLUSIONS: To better respond to population health needs, accreditation is an important first step towards improving the quality of PHC delivery arrangement system. While there is a need to expand the implementation of accreditation to cover all PHC centers in Lebanon, considerations should be given to strengthening their financial arrangements as well.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".