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Record W2172266458 · doi:10.1186/1472-6963-14-s1-s2

Introduction of mobile phones for use by volunteer community health workers in support of integrated community case management in Bushenyi District, Uganda: development and implementation process

2014· article· en· W2172266458 on OpenAlexafffund
David Tumusiime, Gad Agaba, Teddy Kyomuhangi, Jan Finch, Jerome Kabakyenga, Stuart MacLeod

Bibliographic record

VenueBMC Health Services Research · 2014
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of British ColumbiaChild and Family Research Institute
FundersCanadian Institutes of Health ResearchInternational Development Research CentreGovernment of Canada
KeywordsMedicineReferralHealth informaticsmHealthPhonePublic healthMobile phoneCommunity healthHealth careNursingMedical emergencyFamily medicinePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: A substantial literature suggests that mobile phones have great potential to improve management and survival of acutely ill children in rural Africa. The national strategy of the Ugandan Ministry of Health calls for employment of volunteer community health workers (CHWs) in implementation of Integrated Community Case Management (iCCM) of common illnesses (diarrhea, acute respiratory infection, pneumonia, fever/malaria) affecting children under five years of age. A mobile phone enabled system was developed within iCCM aiming to improve access by CHWs to medical advice and to strengthen reporting of data on danger signs and symptoms for acutely ill children under five years of age. Herein critical steps in development, implementation, and integration of mobile phone technology within iCCM are described. METHODS: Mechanisms to improve diagnosis, treatment and referral of sick children under five were defined. Treatment algorithms were developed by the project technical team and mounted and piloted on the mobile phones, using an iterative process involving technical support personnel, health care providers, and academic support. Using a purposefully developed mobile phone training manual, CHWs were trained over an intensive five-day course to make timely diagnoses, recognize clinical danger signs, communicate about referrals and initiate treatment with appropriate essential drugs. Performance by CHWs and the accuracy and completeness of their submitted data was closely monitored post training test period and during the subsequent nine month community trial. In the full trial, the number of referrals and correctly treated children, based on the agreed treatment algorithms, was recorded. Births, deaths, and medication stocks were also tracked. RESULTS AND DISCUSSION: Seven distinct phases were required to develop a robust mobile phone enabled system in support of the iCCM program. Over a nine month period, 96 CHWs were trained to use mobile phones and their competence to initiate a community trial was established through performance monitoring. CONCLUSION: Local information/communication consultants, working in concert with a university based department of pediatrics, can design and implement a robust mobile phone based system that may be anticipated to contribute to efficient delivery of iCCM by trained volunteer CHWs in rural settings in Uganda.

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)0.025
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.004
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 head0.087
GPT teacher head0.509
Teacher spread0.422 · 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 designQualitative
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

Citations37
Published2014
Admission routes2
Has abstractyes

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