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Record W1980217334 · doi:10.2147/rmhp.s4245

Vulnerability and risk perception in the management of HIV/AIDS: Public priorities in a global pandemic

2008· article· en· W1980217334 on OpenAlexaff
Peter Tsasis

Bibliographic record

VenueRisk Management and Healthcare Policy · 2008
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsYork University
Fundersnot available
KeywordsVulnerability (computing)Public healthPandemicRisk perceptionContext (archaeology)Environmental healthIntervention (counseling)Risk managementPerceptionMedicinePublic relationsPsychologyBusinessPolitical scienceGeographyComputer securityNursingCoronavirus disease 2019 (COVID-19)DiseaseComputer science

Abstract

fetched live from OpenAlex

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.116
GPT teacher head0.436
Teacher spread0.320 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2008
Admission routes1
Has abstractyes

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