Sampling sexual and gender minority youth in Canada and the US: Lessons in cost-effectiveness from the UnACoRN internet-based survey (Preprint)
Bibliographic record
Abstract
BACKGROUND: Periodic surveys of sexual and gender minority (SGM) populations are essential for monitoring and investigating health inequities. Recent legislative efforts to ban so-called conversion therapy make it necessary to adapt youth surveys to reach a wider range of SGM populations, 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: We aimed 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 Understanding Affirming Communities, Relationships, and Networks (UnACoRN) web-based survey collected anonymous data in English and French from 9679 mostly SGM respondents in the United States and Canada. Respondents were recruited from March 2022 to August 2022 using word-of-mouth referrals, leaflet distribution, bus advertisements, and 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 (eg, Bitly and Qualtrics) and describe the campaign's effectiveness by recruitment venue based on calculating the cost per completed survey and other secondary metrics. RESULTS: Most participants were recruited through Meta (13,741/16,533, 83.1%), mainly through Instagram; 88.96% (visitors: 14,888/18,179) of our sample reached the survey through paid advertisements. Overall, the cost per survey was lower for Meta than Pornhub or the bus advertisements. Similarly, the proportion of visitors who started the survey was higher for Meta (8492/18,179, 46.7%) than Pornhub (58/18,179, 1.02%). Our subsample of 7037 residents of Canada had a similar geographic distribution to the general population, with an average absolute difference in proportion by province or territory of 1.4% compared to the Canadian census. Our US subsample included 2521 participants from all US states and the District of Columbia. A total of CAD $8571.58 (the currency exchange rate was US $1=CAD $1.25) was spent across 4 paid recruitment channels (Facebook, Instagram, PornHub, and bus advertisements). The most cost-effective tool of recruitment was Instagram, with an average cost per completed survey of CAD $1.48. CONCLUSIONS: UnACoRN recruited nearly 10,000 SGM youth in the United States and Canada, and the cost per survey was CAD $1.48. 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 populations should consider how 2S/LGBTQ-oriented campaigns might deter participation from cisgender or heterosexual people or SGM people not identifying as 2S/LGBTQ, if relevant to their research design. Finally, those with limited resources may select fewer venues with lower cost per completed survey or that appeal more to their specific audience, if needed.
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 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 | 0.093 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".