Links SU-Sex: development of a screening tool for health-risk sexual behaviours related to substance use among men who have sex with men
Bibliographic record
Abstract
Background Men who have sex with men (MSM) have distinctive substance use (SU), which is more often linked to a sexual context than it is for their heterosexual peers. Screening of MSM's SU, its sexual contexts and the associated risks, is of clinical and public health concern. This paper aims to describe the preliminary development of a screening tool for health-risk sexual behaviours related to SU and to make recommendations for its potential use. METHODS: Community-based participatory research and transdisciplinary approaches guided the development process. The Links SU-Sex screening tool is the result of the integration of findings from a scoping review and from four meetings among SU and sexual health experts (n=19), consisting of researchers, community stakeholders, as well as substance-using MSM. RESULTS: The Links SU-Sex questionnaire consists of 64 items divided into 13 components that focus on the links between SU and sexual health. It addresses the contexts in which SU occurs, its frequency, its perceived influence, as well as MSM's concerns about these various links. In accordance with current knowledge, the interpretation of the instrument offers feedback that is based on the respondents' answers to the various components assessed. CONCLUSIONS: The Links SU-Sex represents a potential screening tool that rests on a robust development process supporting its content validity that aims to identify MSM at risk or with concerns surrounding the influence of their SU on their sexual health. The psychometric qualities and the interpretation validity both remain to be established.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".