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Record W2754018041 · doi:10.1186/s12889-017-4758-x

Lay health workers perceptions of an anemia control intervention in Karnataka, India: a qualitative study

2017· article· en· W2754018041 on OpenAlexaff
Arun S. Shet, Paul Jebaraj, Maya Mascarenhas, Merrick Zwarenstein, Maria Rosaria Galanti, Salla Atkins

Bibliographic record

VenueBMC Public Health · 2017
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsWestern University
FundersThe Wellcome Trust DBT India AllianceDepartment of Biotechnology, Ministry of Science and Technology, IndiaWellcome Trust
KeywordsMedicineFocus groupPsychological interventionIntervention (counseling)Implementation researchQualitative researchNursingPublic healthBiostatisticsCluster randomised controlled trialFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Lay health workers (LHWs) are increasingly used to complement health services internationally. Their perceptions of the interventions they implement and their experiences in delivering community based interventions in India have been infrequently studied. We developed a novel LHW led intervention to improve anemia cure rates in rural community dwelling children attending village day care centers in South India. Since the intervention is delivered by the village day care center LHW, we sought to understand participating LHWs' acceptance of and perspectives regarding the intervention, particularly in relation to factors affecting daily implementation. METHODS: We conducted a qualitative study alongside a cluster randomized controlled trial evaluating a complex community intervention for childhood anemia control in Karnataka, South India. Focus group discussions (FGDs) were conducted with trained LHWs assigned to deliver the educational intervention. These were complemented by non-participant observations of LHWs delivering the intervention. Transcripts of the FGDs were translated and analyzed using the framework analysis method. RESULTS: Several factors made the intervention acceptable to the LHWs and facilitated its implementation including pre-implementation training modules, intervention simplicity, and ability to incorporate the intervention into the routine work schedule. LHWs felt that the intervention impacted negatively on their preexisting workload. Fluctuating relationships with mothers weakened the LHWs position as providers of the intervention and hampered efficient implementation, despite the LHWs' highly valued position in the community. Modifiable barriers to the successful implementation of this intervention were seen at two levels. At a broader contextual level, hindering factors included the LHW being overburdened, inadequately reimbursed, and receiving insufficient employer support. At the health system level, lack of streamlining of LHW duties, inability of LHWs to diagnose anemia and temporary shortfalls in the availability of iron supplements constituted potentially modifiable barriers. CONCLUSION: This qualitative study identified some of the practical challenges as experienced by LHWs while delivering a community health intervention in India. Methodologically, it highlights the value of qualitative research in understanding implementation of complex community interventions. On the contextual level, the results indicate that efficient delivery of community interventions will require streamlining of LHW workloads and improved health system infrastructure support. TRIAL REGISTRATION: This trial was registered with ISRCTN.com (identifier: ISRCTN68413407 ) on 23 September 2013.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
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 head0.059
GPT teacher head0.440
Teacher spread0.381 · 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 designQualitative
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

Citations9
Published2017
Admission routes1
Has abstractyes

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