Vulnerability and risk perception in the management of HIV/AIDS: Public priorities in a global pandemic
Bibliographic record
Abstract
Peter Tsasis1,2, N. Nirupama21School of Health Policy and Management, 2School of Administrative Studies, York University, Toronto, Ontario, CanadaAbstract: Understanding the way perception of risk is shaped and constructed is crucial in understanding why it has been so difficult to mitigate the spread of HIV/AIDS. This paper uses the Pressure and Release (PAR) model, used to predict the onset of natural disasters as the conceptual framework. It substitutes vulnerability and risk perception as the trigger factors in the model, in making the case that HIV/AIDS can be characterized as a slow onset disaster. The implications are that vulnerability must be managed and reduced by addressing root causes, dynamic pressures, and unsafe conditions that contribute to the HIV/AIDS pandemic. HIV/AIDS programs must be culturally appropriate and work toward influencing risk perception, while addressing social norms and values that negatively impact vulnerable populations. By impacting cultural and social expectations, individuals will be able to more readily adopt safer sex behaviors. The development of policies and programs addressing the issues in context, as opposed to individual behaviors alone, allows for effective public health intervention. This may have implications for public health measures implemented for combating the spread of HIV/AIDS.Keywords: vulnerability, risk perception, HIV/AIDS, public health intervention
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".