Evidence map›Paper›PMID 25890078›Full record

ArticleBMC health services research2015

Can smartphones and tablets improve the management of childhood illness in Tanzania? A qualitative study from a primary health care worker's perspective.

Amani Flexson Shao, Clotilde Rambaud-Althaus, Ndeniria Swai, Judith Kahama-Maro, Blaise Genton, Valerie D'Acremont, Constanze Pfeiffer

2 registry-linked trialsOpen access · goldAbstract read
In one paragraph

Article in BMC health services research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 35 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 3 pooled it
10.8field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05108831 nacompletednot on this mapstarted 2021, after this paper: background citation

Dynamic Clinical Decision Support Algorithms to Manage Sick Children in Primary Health Care Settings in Rwanda

TypeinterventionalSponsorCenter for Primary Care and Public Health (Unisante), University of Lausanne, SwitzerlandRan2021 to 2024Enrolled85,212ConditionsChild HealthArmsePOCT+
NCT05144763 nacompletednot on this mapstarted 2021, after this paper: background citation

Dynamic Clinical Decision Support Algorithms to Manage Sick Children in Primary Health Care Settings in Tanzania

TypeinterventionalSponsorCenter for Primary Care and Public Health (Unisante), University of Lausanne, SwitzerlandRan2021 to 2024Enrolled92,331ConditionsChild HealthArmsePOCT+
3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 60 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Amani Flexson ShaoSwiss Tropical and Public Health Institute, Basel, Switzerland. shaotz@gmail.com.
Clotilde Rambaud-AlthausSwiss Tropical and Public Health Institute, Basel, Switzerland. rambaud.c@gmail.com.
Ndeniria SwaiCity Medical Office of Health, Dar es Salaam City Council, Tanzania. ndeniriaswai@gmail.com.
Judith Kahama-MaroCity Medical Office of Health, Dar es Salaam City Council, Tanzania.
Blaise GentonSwiss Tropical and Public Health Institute, Basel, Switzerland. blaise.genton@unibas.ch.
Valerie D'AcremontSwiss Tropical and Public Health Institute, Basel, Switzerland. valerie.dacremont@unibas.ch.
Constanze PfeifferSwiss Tropical and Public Health Institute, Basel, Switzerland. constanze.pfeiffer@unibas.ch.
Swiss Tropical and Public Health Institute · CHArusha City Council · TZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe impact of the Integrated Management of Childhood Illness (IMCI) strategy has been less than anticipated because of poor uptake. Electronic algorithms have the potential to improve quality of health care in children. However, feasibility studies about the use of electronic protocols on mobile devices over time are limited. This study investigated constraining as well as facilitating factors that influence the uptake of a new electronic Algorithm for Management of Childhood Illness (ALMANACH) among primary health workers in Dar es Salaam, Tanzania.

methodsA qualitative approach was applied using in-depth interviews and focus group discussions with altogether 40 primary health care workers from 6 public primary health facilities in the three municipalities of Dar es Salaam, Tanzania. Health worker's perceptions related to factors facilitating or constraining the uptake of the electronic ALMANACH were identified.

resultsIn general, the ALMANACH was assessed positively. The majority of the respondents felt comfortable to use the devices and stated that patient's trust was not affected. Most health workers said that the ALMANACH simplified their work, reduced antibiotic prescription and gave correct classification and treatment for common causes of childhood illnesses. Few HWs reported technical challenges using the devices and complained about having had difficulties in typing. Majority of the respondents stated that the devices increased the consultation duration compared to routine practice. In addition, health system barriers such as lack of staff, lack of medicine and lack of financial motivation were identified as key reasons for the low uptake of the devices.

conclusionsThe ALMANACH built on electronic devices was perceived to be a powerful and useful tool. However, health system challenges influenced the uptake of the devices in the selected health facilities.

Indexed as

Attitude of Health PersonnelAdultAttitude to ComputersChild HealthConsumer Health InformationDisease ManagementFemaleFocus GroupsHealth Knowledge, Attitudes, PracticeHealth PersonnelHumansInternetMaleMiddle AgedPrimary Health CareQualitative Research

Identifiers

PMID25890078
PMCPMC4396573
OpenAlexW2047153795

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.