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Record W2613969664 · doi:10.1016/j.cmi.2017.05.007

Implementation of quality management for clinical bacteriology in low-resource settings

2017· review· en· W2613969664 on OpenAlexaff
Barbara Barbé, C.P. Yansouni, Dissou Affolabi, Jan Jacobs

Bibliographic record

VenueClinical Microbiology and Infection · 2017
Typereview
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsAccreditationHealth careMedicineQuality management systemQuality assuranceBacteriologyQuality managementManagement systemOperations managementMedical educationEngineeringPolitical sciencePathologyExternal quality assessment

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.113
metaresearch head score (Gemma)0.170
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.113
Threshold uncertainty score0.598

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1130.170
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.007
Scholarly communication0.0130.006
Open science0.0060.013
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.261
GPT teacher head0.584
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations70
Published2017
Admission routes1
Has abstractyes

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