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Record W2525178311 · doi:10.1186/s12978-016-0216-y

Human resource constraints and the prospect of task-sharing among community health workers for the detection of early signs of pre-eclampsia in Ogun State, Nigeria

2016· article· en· W2525178311 on OpenAlexafffund
David Akeju, Marianne Vidler, John Sotunsa, M. O. Osiberu, E. O. Orenuga, Olufemi T. Oladapo, Akinmade Adepoju, Rahat Qureshi, Diane Sawchuck, Olalekan O. Adetoro, Peter von Dadelszen, Olukayode A. Dada

Bibliographic record

VenueReproductive Health · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsIsland HealthUniversity of British Columbia
FundersUniversity of British ColumbiaBill and Melinda Gates Foundation
KeywordsOgun stateReproductive medicineEnvironmental healthPublic healthHuman resourcesCommunity healthMedicineTask (project management)BusinessState (computer science)EclampsiaPregnancyGeographyBiologyNursingEngineeringPolitical scienceComputer scienceLocal government

Abstract

fetched live from OpenAlex

BACKGROUND: The dearth of health personnel in low income countries has attracted global attention. Ways as to how health care services can be delivered in a more efficient and effective way using available health personnel are being explored. Task-sharing expands the responsibilities of low-cadre health workers and allows them to share these responsibilities with highly qualified health care providers in an effort to best utilize available human resources. This is appropriate in a country like Nigeria where there is a shortage of qualified health professionals and a huge burden of maternal mortality resulting from obstetric complications like pre-eclampsia. This study examines the prospect for task-sharing among Community Health Extension Workers (CHEW) for the detection of early signs of pre-eclampsia, in Ogun State, Nigeria. METHODS: This study is part of a larger community-based trial evaluating the acceptability of community treatment for severe pre-eclampsia in Ogun State, Nigeria. Data was collected between 2011 and 2012 using focus group discussions; seven with CHEWs (n = 71), three with male decision-makers (n = 35), six with community leaders (n = 68), and one with member of the Society of Obstetricians and Gynaecologists of Nigeria (n = 9). In addition, interviews were conducted with the heads of the local government administration (n = 4), directors of planning (n = 4), medical officers (n = 4), and Chief Nursing Officers (n = 4). Qualitative data were analysed using NVivo version 10.0 3 computer software. RESULTS: The non-availability of health personnel is a major challenge, and has resulted in a high proportion of facility-based care performed by CHEWs. As a result, CHEWs often take on roles that are designated for senior health workers. This role expansion has exposed CHEWs to the basics of obstetric care, and has resulted in informal task-sharing among the health workers. The knowledge and ability of CHEWs to perform basic clinical assessments, such as measure blood pressure is not in doubt. Nevertheless, there were divergent views by senior and junior cadres of health practitioners about CHEWs' abilities in providing obstetric care. Similarly, there were concerns by various stakeholders, particularly the CHEWs themselves, on the regulatory restrictions placed on them by the Standing Order. CONCLUSION: Generally, the extent to which obstetric tasks could be shifted to community health workers will be determined by the training provided and the extent to which the observed barriers are addressed. TRIAL REGISTRATION: NCT01911494.

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)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.316
Teacher spread0.292 · 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 designObservational
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

Citations23
Published2016
Admission routes2
Has abstractyes

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