Implementation of quality management for clinical bacteriology in low-resource settings
Bibliographic record
Abstract
BACKGROUND: The declining trend of malaria and the recent prioritization of containment of antimicrobial resistance have created a momentum to implement clinical bacteriology in low-resource settings. Successful implementation relies on guidance by a quality management system (QMS). Over the past decade international initiatives were launched towards implementation of QMS in HIV/AIDS, tuberculosis and malaria. AIMS: To describe the progress towards accreditation of medical laboratories and to identify the challenges and best practices for implementation of QMS in clinical bacteriology in low-resource settings. SOURCES: Published literature, online reports and websites related to the implementation of laboratory QMS, accreditation of medical laboratories and initiatives for containment of antimicrobial resistance. CONTENT: Apart from the limitations of infrastructure, equipment, consumables and staff, QMS are challenged with the complexity of clinical bacteriology and the healthcare context in low-resource settings (small-scale laboratories, attitudes and perception of staff, absence of laboratory information systems). Likewise, most international initiatives addressing laboratory health strengthening have focused on public health and outbreak management rather than on hospital based patient care. Best practices to implement quality-assured clinical bacteriology in low-resource settings include alignment with national regulations and public health reference laboratories, participating in external quality assurance programmes, support from the hospital's management, starting with attainable projects, conducting error review and daily bench-side supervision, looking for locally adapted solutions, stimulating ownership and extending existing training programmes to clinical bacteriology. IMPLICATIONS: The implementation of QMS in clinical bacteriology in hospital settings will ultimately boost a culture of quality to all sectors of healthcare 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 | 0.113 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".