Evidence map›Paper›PMID 31908861›Full record

ArticleBMJ global health2019

The supervisory relationships of community health workers in primary health care: social network analysis of ward-based outreach teams in Ngaka Modiri Molema District, South Africa.

Tumelo Assegaai, Helen Schneider

Abstract read
In one paragraph

Article in BMJ global health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
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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

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

2 authors.

Tumelo AssegaaiSchool of Public Health, University of the Western Cape Faculty of Community and Health Sciences, Cape Town, South Africa.ORCID 0000-0003-2493-6025
Helen SchneiderSchool of Public Health, University of the Western Cape, Cape Town, South Africa.ORCID 0000-0002-0418-1828

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSupportive supervision remains a key challenge to the sustainability of community health worker (CHW) programmes globally. The aim of the study was to identify critical actors and patterns of relationships in the supervision of ward-based outreach teams (WBOT) in a rural South African district.

methodsA cross-sectional study of social and professional relationships of WBOTs with other primary health care (PHC) system actors was conducted using a social network analysis (SNA) approach. A structured questionnaire was distributed to CHWs (37), WBOT team leaders (3), PHC facility managers (5) and PHC local area managers (2) (total n=47) assessing interaction patterns of supportive supervision, namely management, development and support.

resultsThe supportive supervision system pivoted around team leaders, who were nurse cadres and who ensured internal cohesion and support among WBOT members. The network patterns also showed the extent of peer support between CHWs in WBOTs. PHC facility staff and middle managers in the subdistrict did not appear to play active roles in the supervision of CHWs and their team leaders. However, there were exceptions, with WBOTs drawing on sympathetic cadres identified among the PHC facility staff for support.

conclusionSupportive supervision of CHWs can be thought of as a system of horizontal and vertical relationships that go beyond just one supervisor-supervisee interaction. In this study, supervisory relationships within teams functioned better than those between teams and the rest of the PHC system. Understanding these relationships is key to designing effective supportive supervision in CHW programmes. SNA can be a valuable approach in identifying the relationships to be strengthened.

Indexed as

community health workerssocial network analysissupervisionsupportward-based outreach team

Identifiers

PMID31908861
PMCPMC6936529

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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.