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Record W6977277346 · doi:10.60692/cveap-qvk07

Effect of Educational Outreach Timing and Duration on Facility Performance for Infectious Disease Care in Uganda: A Trial with Pre-Post and Cluster Randomized Controlled Components

2015· article· en· W6977277346 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOutreachDuration (music)Health careInfectious disease (medical specialty)Cluster (spacecraft)Health facilityDisease controlHealth worker

Abstract

fetched live from OpenAlex

Background Classroom-based learning is often insufficient to ensure high quality care and application of health care guidelines. Educational outreach is garnering attention as a supplemental method to enhance health care worker capacity, yet there is little information about the timing and duration required to improve facility performance. We sought to evaluate the effects of an infectious disease training program followed by either immediate or delayed on-site support (OSS), an educational outreach approach, on nine facility performance indicators for emergency triage, assessment, and treatment; malaria; and pneumonia. We also compared the effects of nine monthly OSS visits to extended OSS, with three additional visits over six months. Methods This study was conducted at 36 health facilities in Uganda, covering 1,275,960 outpatient visits over 23 months. From April 2010 to December 2010, 36 sites received infectious disease training; 18 randomly selected sites in arm A received nine monthly OSS visits (immediate OSS) and 18 sites in arm B did not. From March 2011 to September 2011, arm A sites received three additional visits every two months (extended OSS), while the arm B sites received eight monthly OSS visits (delayed OSS). We compared the combined effect of training and delayed OSS to training followed by immediate OSS to determine the effect of delaying OSS implementation by nine months. We also compared facility performance in arm A during the extended OSS to immediate OSS to examine the effect of additional, less frequent OSS. Results Delayed OSS, when combined with training, was associated with significant pre/post improvements in four indicators: outpatients triaged (44% vs. 87%, aRR = 1.54, 99% CI = 1.11, 2.15); emergency and priority patients admitted, detained, or referred (16% vs. 31%, aRR = 1.74, 99% CI = 1.10, 2.75); patients with a negative malaria test result prescribed an antimalarial (53% vs. 34%, aRR = 0.67, 99% CI = 0.55, 0.82); and pneumonia suspects assessed for pneumonia (6% vs. 27%, aRR = 2.97, 99% CI = 1.44, 6.17). Differences between the delayed OSS and immediate OSS arms were not statistically significant for any of the nine indicators (all adjusted relative RR (aRRR) between 0.76–1.44, all p>0.06). Extended OSS was associated with significant improvement in two indicators (outpatients triaged: aRR = 1.09, 99% CI = 1.01; emergency and priority patients admitted, detained, or referred: aRR = 1.22, 99% CI = 1.01, 1.38) and decline in one (pneumonia suspects assessed for pneumonia: aRR: 0.93; 99% CI = 0.88, 0.98). Conclusions Educational outreach held up to nine months after training had similar effects on facility performance as educational outreach started within one month post-training. Six months of bi-monthly educational outreach maintained facility performance gains, but incremental improvements were heterogeneous.

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)0.006
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.024
GPT teacher head0.235
Teacher spread0.212 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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