Sampling sexual and gender minority youth in Canada and the US: Lessons from the UnACoRN internet-based survey (Preprint)
Bibliographic record
Abstract
BACKGROUND Periodic surveys of sexual and gender minority (SGM) populations have been an essential tool for monitoring and investigating health inequities. Recent legislative efforts to ban so-called ‘conversion therapy’ have introduced the need to adapt youth surveys to reach a wider range of SGM, including those <18 years of age and those who may not adopt an explicit Two-Spirit, lesbian, gay, bisexual, transgender and queer (2S/LGBTQ) identity. OBJECTIVE To share our experiences in recruiting SGM youth through multiple in-person and online channels and to share lessons learned for future researchers. METHODS The UnACoRN (Understanding Affirming Communities, Relationships, and Networks) Survey collected anonymous data in English and French from 9,679 people (mostly SGM). Respondents were recruited Mar-Aug 2022 using word-of-mouth referrals, leaflet distribution, and buses advertisements with paid and unpaid campaigns on social media and a pornography website. We analyzed the metadata provided by these and other online resources we used for recruitment (e.g., Bitly, Qualtrics) and provided an overview of the campaign effectiveness by recruitment venue by calculating the cost per completed survey and other secondary metrics. RESULTS Most participants were recruited through Meta (83.2%), mainly through Instagram. 88.96% of our sample reached the survey through paid advertisements. Overall, the cost per survey was lower for Meta than Pornhub or the bus ads. Similarly, the proportion of visitors who started the survey was higher for Meta (46.70%) than Pornhub (1.02%). CONCLUSIONS Researchers using online recruitment strategies should be aware of the differences in campaign management each website or social media platform offers and be prepared to engage with their framing (content selection and delivery) to correct any imbalances derived from it. Those who focus on SGM should consider how 2S/LGBTQ-oriented campaigns might deter participation from cis-het people or SGM not identifying as 2S/LGBTQ, if relevant to their research design. Finally, those with limited resources may select fewer venues with a lower cost per completed survey or those that appeal more to their specific audience, if needed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".