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Record W2134033974 · doi:10.1136/sextrans-2012-050774

Translating knowledge from Pakistan's second generation surveillance system to other global contexts

2012· article· en· W2134033974 on OpenAlexaffabout
A Adrien, Laura H. Thompson, Chris Archibald, Paul Sandstrom, Michelle L. Munro‐Kramer, Faran Emmanuel, James Blanchard

Bibliographic record

VenueSexually Transmitted Infections · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsPublic Health Agency of CanadaUniversity of ManitobaHealth Sciences CentreMcGill UniversitySante Montreal
Fundersnot available
KeywordsMedicineData scienceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: From 2004 to 2011, a collaborative project was undertaken to enhance the capacity of the Government of Pakistan to implement an effective second-generation surveillance system for HIV/AIDS, known as the HIV/AIDS Surveillance Project (HASP). In four separate rounds, behavioural questionnaires were administered among injection drug users, and female, male and hijra (transgender) sex workers. Dried blood spots were collected for HIV testing. METHODS: Through interviews with project staff in Pakistan and Canada, we have undertaken a critical review of the role of HASP in generating, using and translating knowledge, with an emphasis on capacity building within both the donor and recipient countries. We also documented ongoing and future opportunities for the translation of knowledge produced through HASP. RESULTS: Knowledge translation activities have included educational workshops and consultations held in places as diverse as Colombia and Cairo, and the implementation of HASP methodologies in Asia, the Middle East and sub-Saharan Africa. HASP methodologies have been incorporated in multiple WHO reports. Importantly, the donor country, Canada, has benefited in significant ways from this partnership. Operational and logistical lessons from HASP have, in turn, improved how surveillance is performed in Canada. Through this project, significant capacity was built among the staff of HASP, non-governmental organisations which were engaged as implementation partners, data coordination units which were established in each province, and in the laboratory. As is to be expected, different organisations have different agendas and priorities, requiring negotiation, at times, to ensure the success of collaborative activities. Overall, there has been considerable interest in and opportunities made for learning about the methodologies and approaches employed by HASP. CONCLUSIONS: Generally, the recognition of the strengths of the approaches and methodologies used by HASP has ensured an appetite for opportunities of mutual learning.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.340
Teacher spread0.307 · 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 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

Citations3
Published2012
Admission routes2
Has abstractyes

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