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Record W1966334428 · doi:10.3402/gha.v7.23594

Considerations for preparing a randomized population health intervention trial: lessons from a South African–Canadian partnership to improve the health of health workers

2014· article· en· W1966334428 on OpenAlexafffundabout
Annalee Yassi, Lyndsay M. O’Hara, Michelle Engelbrecht, Kerry Uebel, Letshego E. Nophale, Elizabeth Bryce, Jane A. Buxton, Jacob S. Siegel, Jerry Spiegel

Bibliographic record

VenueGlobal Health Action · 2014
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia
FundersCanadian Institutes of Health ResearchInternational Development Research CentreUnited States Agency for International Development
KeywordsRandomized controlled trialPopulationMedicinePsychological interventionGlobal healthConfidentialityNursingPublic relationsFamily medicineMedical educationPublic healthEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Community-based cluster-randomized controlled trials (RCTs) are increasingly being conducted to address pressing global health concerns. Preparations for clinical trials are well-described, as are the steps for multi-component health service trials. However, guidance is lacking for addressing the ethical and logistic challenges in (cluster) RCTs of population health interventions in low- and middle-income countries. OBJECTIVE: We aimed to identify the factors that population health researchers must explicitly consider when planning RCTs within North-South partnerships. DESIGN: We reviewed our experiences and identified key ethical and logistic issues encountered during the pre-trial phase of a recently implemented RCT. This trial aimed to improve tuberculosis (TB) and Human Immunodeficiency Virus (HIV) prevention and care for health workers by enhancing workplace assessment capability, addressing concerns about confidentiality and stigma, and providing onsite counseling, testing, and treatment. An iterative framework was used to synthesize this analysis with lessons taken from other studies. RESULTS: The checklist of critical factors was grouped into eight categories: 1) Building trust and shared ownership; 2) Conducting feasibility studies throughout the process; 3) Building capacity; 4) Creating an appropriate information system; 5) Conducting pilot studies; 6) Securing stakeholder support, with a view to scale-up; 7) Continuously refining methodological rigor; and 8) Explicitly addressing all ethical issues both at the start and continuously as they arise. CONCLUSION: Researchers should allow for the significant investment of time and resources required for successful implementation of population health RCTs within North-South collaborations, recognize the iterative nature of the process, and be prepared to revise protocols as challenges emerge.

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)0.584
metaresearch head score (Gemma)0.482
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.492
Threshold uncertainty score0.978

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5840.482
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0040.005
Science and technology studies0.0290.020
Scholarly communication0.0210.014
Open science0.0080.015
Research integrity0.0180.025
Insufficient payload (model declined to judge)0.0040.001

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 head0.084
GPT teacher head0.423
Teacher spread0.339 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations25
Published2014
Admission routes3
Has abstractyes

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