Potential Contribution of the Social Representations Theory to HIV Prevention in Uganda: Theoretical and Empirical Issues
Bibliographic record
Abstract
Behavioural change responses to prevent HIV known as ABC (A= Abstinence; B=be Faithful; C=Condom use) in Uganda are widely acclaimed nationally and internationally. However, to date little is known about the mechanisms by which behavioural changes occur, specifically among traditionally high-risk groups like sex workers. Despite possessing accurate knowledge about ABC, these groups experience very high rates of HIV infection still unexplained empirically. Based on a literature review, this paper proposes that the social representations theory (SRT) can inform research into the mechanisms of behavioural change, particularly social representations or worldviews underlying local sexual practices. This understanding can improve efforts in non-European contexts seeking to motivate individual competencies to change risky sexual attitudes and relations. Existing HIV approaches study mainly interindividual cognitive differences regarding HIV responses creating theoretical and empirical problems obscuring the analysis of culturally sensitive meanings shaping local competence in real world conditions. SRT, which primarily analyses the mechanisms through which scientific ideas like ABC are translated into ordinary thinking, can inform empirical investigations about worldviews shaping meanings that enhance or discourage changing risky sexual relations. Researchers can develop tools to operationalise the social and cognitive aspects of SRT to improve understandings of behavioural change processes and enhance the efficacy of ABC. Despite limitations, SRT opens up a wider space for social research regarding behavioural change responses, key to HIV prevention in countries facing extreme poverty and very high birth rates, particularly Uganda.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".