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Record W2809019069 · doi:10.1186/s12978-018-0532-5

The feasibility of task-sharing the identification, emergency treatment, and referral for women with pre-eclampsia by community health workers in India

2018· article· en· W2809019069 on OpenAlexafffund
Umesh Charanthimath, Marianne Vidler, Geetanjali Katageri, Umesh Y Ramadurg, Chandrashekhar Karadiguddi, Avinash Kavi, Anjali Joshi, Geetanjali Mungarwadi, Sheshidhar Bannale, Sangamesh Rakaraddi, Diane Sawchuck, Rahat Qureshi, Sumedha Sharma, Beth A. Payne, Peter von Dadelszen, Richard J. Derman, Laura A. Magee, Shivaprasad S. Goudar, Ashalata Mallapur, Mrutyunjaya B. Bellad, Zulfiqar A Bhutta, Sheela Naik, Anis Mulla, Namdev Kamle, Vaibhav B Dhamanekar, Sharla Drebit, Chirag Kariya, Tang Lee, Jing Li, Mansun Lui, Asif Raza Khowaja, Domena Tu, Amit Revankar

Bibliographic record

VenueReproductive Health · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsIsland HealthUniversity of British Columbia
FundersUniversity of British ColumbiaBill and Melinda Gates Foundation
KeywordsMedicineReferralReproductive medicineFamily medicineHealth careFocus groupCommunity healthPublic healthEclampsiaPregnancyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Hypertensive disorders are the second highest direct obstetric cause of maternal death after haemorrhage, accounting for 14% of maternal deaths globally. Pregnancy hypertension contributes to maternal deaths, particularly in low- and middle-income countries, due to a scarcity of doctors providing evidence-based emergency obstetric care. Task-sharing some obstetric responsibilities may help to reduce the mortality rates. This study was conducted to assess acceptability by the community and other healthcare providers, for task-sharing by community health workers (CHW) in the identification and initial care in hypertensive disorders in pregnancy. METHODS: This study was conducted in two districts of Karnataka state in south India. A total of 14 focus group discussions were convened with various community representatives: women of reproductive age (N = 6), male decision-makers (N = 2), female decision-makers (N = 3), and community leaders (N = 3). One-to-one interviews were held with medical officers (N = 2), private healthcare OBGYN specialists (N = 2), senior health administrators (N = 2), Taluka (county) health officers (N = 2), and obstetricians (N = 4). All data collection was facilitated by local researchers familiar with the setting and language. Data were subsequently transcribed, translated and analysed thematically using NVivo 10 software. RESULTS: There was strong community support for home visits by CHW to measure the blood pressure of pregnant women; however, respondents were concerned about their knowledge, training and effectiveness. The treatment with oral antihypertensive agents and magnesium sulphate in emergencies was accepted by community representatives but medical practitioners and health administrators had reservations, and insisted on emergency transport to a higher facility. The most important barriers for task-sharing were concerns regarding insufficient training, limited availability of medications, the questionable validity of blood pressure devices, and the ability of CHW to correctly diagnose and intervene in cases of hypertensive disorders of pregnancy. CONCLUSION: Task-sharing to community-based health workers has potential to facilitate early diagnosis of the hypertensive disorders of pregnancy and assist in the provision of emergency care. We identified some facilitators and barriers for successful task-sharing of emergency obstetric care aimed at reducing mortality and morbidity due to hypertensive disorders of pregnancy.

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)0.016
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.030
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.385
Teacher spread0.330 · 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 designNon-randomized 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

Citations21
Published2018
Admission routes2
Has abstractyes

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