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Record W3038211389 · doi:10.1200/jgo.19.00190

Engaging Patients for Clinical Trials in Africa: Patient-Centered Approaches

2020· article· en· W3038211389 on OpenAlexaff
Miriam Mutebi, Dicey Scroggins, Virgil Simons, Naomi Ohene Oti, Nazik Hammad

Bibliographic record

VenueJCO Global Oncology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsQueen's University
Fundersnot available
KeywordsClinical trialSociocultural evolutionAutonomyCommunity engagementResource (disambiguation)LiteracyMedicineHealth literacyMedical educationNursingPsychologyPublic relationsHealth carePolitical sciencePedagogyComputer sciencePathology

Abstract

fetched live from OpenAlex

Clinical trials in oncology are an emergent field in sub-Saharan Africa. There is a long history of clinical trials in high-income countries (HICs), with increasing attempts to develop patient-centric approaches and to evaluate patient-centered outcomes. The challenge remains as to how these trends could be adopted in low-resource settings and adapted to best fit the different health ecosystems that coexist on the African continent. Models that evaluate patient-related outcomes and measures and that are used in HICs must be modified, adopted, and adapted to suit the diverse populations and the low-resource settings in most of the continent. Patient engagement in clinical trials in Africa must be well nuanced, and it demands innovation and application of models that consider established but tailored notions/principles of patient and community engagement and the unique sociocultural aspects of different populations. It also must be linked to strategies that aim to improve patient education, health literacy, and access to services and to encourage and protect patient autonomy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.857
GPT teacher head0.591
Teacher spread0.266 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations8
Published2020
Admission routes1
Has abstractyes

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