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Record W2094039814 · doi:10.1057/jphp.2014.30

Building capacities of elected national representatives to interpret and to use evidence for health-related policy decisions: A case study from Botswana

2014· article· en· W2094039814 on OpenAlexafffund
Anne Cockcroft, Mokgweetsi Masisi, Lehana Thabane, Neil Andersson

Bibliographic record

VenueJournal of Public Health Policy · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcGill UniversityMcMaster University
FundersCanadian Institutes of Health ResearchInternational Development Research CentreGovernment of Canada
KeywordsPsychological interventionEvidence-based policyPublic healthHealth policyEvidence-based practicePublic health lawSocial policySession (web analytics)Unit (ring theory)MedicineEvidence-based medicineHealth services researchEnvironmental healthPolitical sciencePublic relationsEconomic growthInternational healthMEDLINEPsychologyNursingAlternative medicineBusinessLawEconomics

Abstract

fetched live from OpenAlex

Elected national representatives make decisions to fund health programmes, but may lack skills to interpret evidence on health-related topics. In 2011, we surveyed the 61 members of Botswana's Parliament about their use of epidemiological evidence, then provided two half-days of training about using evidence. We included the importance of counter-factual evidence, the number needed to treat, and unit costs of interventions. A further session in 2012 covered evidence about the HIV epidemic in Botswana and planning the best mix of interventions to reduce new HIV infections. The 27 respondents reported they lacked good quality, timely evidence, and had difficulty interpreting and using evidence. Thirty-six, including seven ministers, attended one or both trainings. They participated actively and their evaluation was positive. Our experience in Botswana could potentially be extended to other countries in the region to support evidence-based efforts to tackle the HIV epidemic.

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)0.023
metaresearch head score (Gemma)0.235
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.235
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.758
GPT teacher head0.718
Teacher spread0.039 · 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 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
Published2014
Admission routes2
Has abstractyes

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