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Record W2801420218 · doi:10.1111/irv.12569

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

2018· article· en· W2801420218 on OpenAlexaff
Janet Dı́az, Justin R. Ortiz, Paula Lister, Nahoko Shindo, Neill K. J. Adhikari

Bibliographic record

VenueInfluenza and Other Respiratory Viruses · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersWorld Health OrganizationUnited States Agency for International Development
KeywordsCritically illIntensive care medicineMedicineIntensive careMedical emergency

Abstract

fetched live from OpenAlex

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.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.098
GPT teacher head0.409
Teacher spread0.311 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2018
Admission routes1
Has abstractyes

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