Strengthening the clinical laboratory workforce in Cambodia: a case study of a mixed-method in-service training program to improve laboratory quality management system oversight
Bibliographic record
Abstract
BACKGROUND: Laboratory diagnostic testing service delivery and compliance with international standards for laboratory quality are directly influenced by laboratory workforce competency. Many hospital laboratories in constrained resource settings such as Cambodia struggle to cope with the training needs of laboratory professionals in an environment of competing healthcare development priorities. Resource-limited countries need an adaptable and effective approach to provide laboratory professionals with job-specific quality oversight training to ensure the accuracy, timeliness, and reliability of diagnostic services. CASE PRESENTATION: Here, we describe the results of an in-service training and mentoring program conducted with the Cambodia Ministry of Health at 12 tertiary-level hospital laboratories to drive improvements in laboratory quality management systems toward ISO 15189 accreditation, which demonstrated significant progress between baseline and outcome audits in a concurrent study. This case study describes the program, and evaluates how the four primary activities, including actionable gap assessments and planning, centralized and in situ training curriculum, in-person mentoring, and remote tele-mentoring via video communication technologies, contributed towards quality improvement in the participating laboratories. We evaluated participant responses to Likert scale and free response questions from program and training evaluation surveys, and we used thematic analysis to develop a model of best practices within the program's four primary activities to inform future training approaches. Of these activities, participants agreed most highly that in-person visits and planning based on gap assessments contributed to their learning and ability to improve laboratory operations. Tele-mentoring was rated lowest by participants, who were critical of excessive group dialogue and distraction during web-conferencing; however, feedback suggests both in-person and remote mentoring contribute to continuing education, accountability to action, and peer collaboration and problem solving to improve workforce efforts toward improved quality management systems. CONCLUSIONS: We recommend here a package of in-service training activities for laboratory quality management system improvement initiatives in resource constrained settings that includes needs-based curricula and personalized action plans for participants; interactive and on-site training workshops; and in-person mentoring, complemented with well managed and regular tele-mentoring that focuses on knowledge retention, accountability to goals, and collaborative problem solving. Our model presents an adaptable approach to human resource development for quality improvement in medical laboratories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".