Measurement of Improvement Achieved by Participation in International Laboratory Accreditation in Sub-Saharan Africa
Bibliographic record
Abstract
OBJECTIVES: As part of the ISO 15189:2007 accreditation process, the Aga Khan University Hospital Nairobi laboratory became the first internationally accredited hospital laboratory in sub-Saharan Africa outside South Africa in 2011 through the South Africa National Accreditation System. METHODS: Seven preanalytic, 10 analytic, eight postanalytic, and five administrative performance parameters were monitored from 2009 to 2012 to measure the impact of the accreditation process. RESULTS: Most measures in all four categories showed substantial improvement. The seven preanalytic measures all showed major improvement-between a quarter and a half sigma. Real but less dramatic improvement appeared in analytic and postanalytic measures, but greater than one sigma decrease in analytic "procedure violations" and a three-quarter sigma decrease in excessive turnaround time were noted in these categories. Administrative improvements included dramatic decreases in misdirected and missing reports and complaints. CONCLUSIONS: This study demonstrates the correlation of the accreditation process with improvement in quality measures in a low-resource region.
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 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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".