Development of a short course on management of critically ill patients with acute respiratory infection and impact on clinician knowledge in resource‐limited intensive care units
Bibliographic record
Abstract
BACKGROUND: The 2009 influenza A (H1N1) pandemic caused surges of patients in intensive care units (ICUs) in resource-limited settings. Several Ministries of Health requested clinical management guidance from the World Health Organization (WHO), which had not previously developed guidance regarding critically ill patients. OBJECTIVE: To assess the acceptability and impact on knowledge of a short course about the management of critically ill patients with acute respiratory infections complicated by sepsis or acute respiratory distress syndrome delivered to clinicians in resource-limited ICUs. METHODS: Over 4 years (2009-2013), WHO led the development, piloting, implementation and preliminary evaluation of a 3-day course that emphasized patient management based on evidence-based guidelines and used interactive adult-learner teaching methodology. International content experts (n = 35) and instructional designers contributed to development. We assessed participants' satisfaction and content knowledge before and after the course. RESULTS: The course was piloted among clinicians in Trinidad and Tobago (n = 29), Indonesia (n = 38) and Vietnam (n = 86); feedback from these courses contributed to the final version. In 2013, inaugural national courses were delivered in Tajikistan (n = 28), Uzbekistan (n = 39) and Azerbaijan (n = 30). Participants rated the course highly and demonstrated increased immediate content knowledge after (vs before) course completion (P < .001). CONCLUSIONS: We found that it was feasible to create and deliver a focused critical care short course to clinicians in low- and middle-income countries. Collaboration between WHO, clinical experts, instructional designers, Ministries of Health and local clinician-leaders facilitated course delivery. Future work should assess its impact on longer-term knowledge retention and on processes and outcomes of care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".