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Record W2552564775 · doi:10.1186/s12913-016-1891-7

Shifting human resources for health in the context of ART provision: qualitative and quantitative findings from the Lablite baseline study

2016· article· en· W2552564775 on OpenAlexaff
Misheck J. Nkhata, M. Muzambi, Deborah Ford, Adrienne K. Chan, George Abongomera, Harriet Namata, Ivan Mambule, Annabelle South, Paul Revill, Caroline Grundy, Travor Mabugu, Levison Chiwaula, James Hakim, Cissy Kityo, Andrew Reid, Elly Katabira, Sumeet Sodhi, Charles F. Gilks, Diana M. Gibb, Janet Seeley, Fabian Cataldo

Bibliographic record

VenueBMC Health Services Research · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsToronto Western HospitalUniversity Health NetworkHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersMedical Research CouncilDepartment for International Development
KeywordsMedicineContext (archaeology)Focus groupHuman resourcesWorkloadHealth careHealth administrationNursingHealth services researchBaseline (sea)Nursing researchQualitative researchService delivery frameworkPublic healthService (business)Economic growthBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Lablite is an implementation project supporting and studying decentralized antiretroviral therapy (ART) rollout to rural communities in Malawi, Uganda and Zimbabwe. Task shifting is one of the strategies to deal with shortage of health care workers (HCWs) in ART provision. Evaluating Human Resources for Health (HRH) optimization is essential for ensuring access to ART. The Lablite project started with a baseline survey whose aim was to describe and compare national and intercountry delivery of ART services including training, use of laboratories and clinical care. METHODS: A cross-sectional survey was conducted between October 2011 and August 2012 in a sample of 81 health facilities representing different regions, facility levels and experience of ART provision in Malawi, Uganda and Zimbabwe. Using a questionnaire, data were collected on facility characteristics, human resources and service provision. Thirty three (33) focus group discussions were conducted with HCWs in a subset of facilities in Malawi and Zimbabwe. RESULTS: The survey results showed that in Malawi and Uganda, primary care facilities were run by non-physician clinical officers/medical assistants while in Zimbabwe, they were run by nurses/midwives. Across the three countries, turnover of staff was high especially among nurses. Between 10 and 20% of the facilities had at least one clinical officer/medical assistant leave in the 3 months prior to the study. Qualitative results show that HCWs in ART and non-ART facilities perceived a shortage of staff for all services, even prior to the introduction of ART provision. HCWs perceived the introduction of ART as having increased workload. In Malawi, the number of people on ART and hence the workload for HCWs has further increased following the introduction of Option B+ (ART initiation and life-long treatment for HIV positive pregnant and lactating women), resulting in extended working times and concerns that the quality of services have been affected. For some HCWs, perceived low salaries, extended working schedules, lack of training opportunities and inadequate infrastructure for service provision were linked to low job satisfaction and motivation. CONCLUSIONS: ART has been decentralized to lower level facilities in the context of an ongoing HRH crisis and staff shortage, which may compromise the provision of high-quality ART services. Task shifting interventions need adequate resources, relevant training opportunities, and innovative strategies to optimize the operationalization of new WHO treatment guidelines which continue to expand the number of people eligible for ART.

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)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0070.007
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0010.002
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 head0.165
GPT teacher head0.550
Teacher spread0.385 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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