Perceived difficulty of getting help to reduce or abstain from substances among sexual and gender minority men who have sex with men (SGMSM) and use methamphetamine during the early period of the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: This study examined the perceived difficulty of getting help with substance use among sexual and gender minorities who have sex with men (SGMSM) who use methamphetamine during the early COVID-19 period. METHODS: SGMSM, aged 18+, who reported sex with a man and methamphetamine use in the past 6 months were recruited to complete an online survey using online advertisements. Ordinal regression models examined predictors of greater perceived difficulty of getting help. Explanatory variables included participant characteristics (i.e., age, HIV status, ethnicity, sexuality, gender, region, income) and variables assessing patterns of methamphetamine use (i.e., frequency, % time methamphetamine is used alone and during sex; perceived need for help) and patterns of healthcare access (i.e., regular provider, past substance use service utilization). RESULTS: Of 376 participants, most were gay-identified (76.6%), white (72.3%), cisgender (93.6%), and had annual incomes of less than $60,000 CAD (68.9%). Greater perceived difficulty of getting help was associated with having lower income, sometimes using methamphetamine prior to or during sex, and greater perceived need for help. CONCLUSION: Based on these results, we urge greater investments in one-stop, low-barrier, culturally-appropriate care for SGMSM who use methamphetamine. This is especially important given that participants who perceive themselves as needing help to reduce or abstain from substance use perceive the greatest difficulty of getting such help.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".