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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».