Beliefs as predictors of condom use by injecting drug users in treatment
Bibliographic record
Abstract
This study was conducted to clarify (1) the extent to which health beliefs selected from Protection Motivation Theory can combine to correctly classify 72 injecting drug users (IDUs) as condom users or non-users and (2) which of the beliefs ('vulnerability to a regular partner', 'vulnerability to a casual partner', 'self-efficacy', 'response efficacy', 'response costs' and 'social norms') were most influential in this distinction. Results of a logistic regression indicated that these beliefs were significant predictors of condom use. Overall, 83.3% of participants were correctly classified according to condom use, with condom 'non-users' being more accurately predicted (94.0%) than 'users' (59.1%). 'Vulnerability to a regular partner' and 'social norms' were significant multivariate and univariate predictors of condom use, and 'response costs' were significant univariate predictors. IDUs were confident of their ability to use condoms, considered themselves highly vulnerable to HIV infection from casual partners and were confident in the efficacy of condoms to protect them from AIDS. However, the majority of IDUs were not condom users, particularly with 'regular' partners. Findings suggest that HIV prevention programmes should target beliefs regarding risks from known partners, perceived norms and negative consequences of condom use in order to increase condom use by IDUs in treatment.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".