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Record W6939164544 · doi:10.60692/fvh04-tzh20

Effectiveness of educational outreach in infectious diseases management: a cluster randomized trial in Uganda

2016· article· en· W6939164544 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMalariaOutreachTriageWorkloadRandomized controlled trialCluster (spacecraft)Cluster randomised controlled trialHealth workerTropical medicine

Abstract

fetched live from OpenAlex

Integrated Infectious Diseases Capacity Building Evaluation (IDCAP) teams designed and implemented two health worker in-service training approaches: 1) an off-site classroom-based integrated management of infectious diseases (IMID) course with distance learning aspects, and 2) on-site support (OSS), an educational outreach intervention. We tested the effects of OSS on workload and 12 facility performance indicators for emergency triage assessment and treatment, HIV testing, and malaria and pneumonia case management among outpatients by two subgroups: 1) mid-level practitioners (MLP) who attended IMID training (IMID-MLP) and 2) health workers who did not (No-IMID).Thirty-six health facilities participated in the IDCAP trial, with 18 randomly assigned to Arm A and 18 to Arm B. Two MLP in both arms received IMID. All providers at Arm A facilities received nine monthly OSS visits from April to December 2010 while Arm B did not. From November 2009 to December 2010, 777,667 outpatient visits occurred. We analyzed 669,580 (86.1 %) outpatient visits, where provider cadre was reported. Treatment was provided by 64 IMID-MLP and 1,515 No-IMID providers. The effect of OSS was measured by the difference in pre/post changes across arms after controlling for covariates (adjusted ratio of relative risks = a RRR).The effect of OSS on patients-per-provider-per-day (workload) among IMID-MLP (aRRR = 1.21; p = 0.48) and No-IMID (aRRR = 0.90; p = 0.44) was not statistically significant. Among IMID-MLP, OSS was effective for three indicators: malaria cases receiving an appropriate antimalarial (aRRR = 1.26, 99 % CI = 1.02-1.56), patients with negative malaria test result prescribed an antimalarial (aRRR = 0.49, 99 % CI = 0.26-0.92), and patients with acid-fast bacilli smear negative result receiving empiric treatment for acute respiratory infection (aRRR = 2.04, 99 % CI = 1.06-3.94). Among No-IMID, OSS was effective for two indicators: emergency and priority patients admitted, detained or referred (aRRR = 2.12, 99 % CI = 1.05-4.28) and emergency patients receiving at least one appropriate treatment (aRRR = 1.98, 99 % CI = 1.21-3.24).Effects of OSS on workload were not statistically significant. Significant OSS effects on facility performance across subgroups were heterogeneous. OSS supported MLP who diagnosed and treated patients to apply IMID knowledge. For other providers, OSS supported team work to manage emergency patients. This evidence on OSS effectiveness could inform interventions to improve health workers' capacity to deliver better quality infectious diseases 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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.007
GPT teacher head0.208
Teacher spread0.201 · 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 designRandomized trial
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

Citations0
Published2016
Admission routes1
Has abstractyes

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