Talk or Text to Tell? How Young Adults in Canada and South Africa Prefer to Receive STI Results, Counseling, and Treatment Updates in a Wireless World
Bibliographic record
Abstract
Young adults often lack access to confidential, long-lasting, and nonjudgmental interactions with sexual health professionals at brick-and-mortar clinics. To ensure that patients return for their STI test results, post-result counseling, and STI-related information, computer-mediated health intervention programming allows them to receive sexual health information through onsite computers, the Internet, and mobile phone calls and text messages. To determine whether young adults (age: M = 21 years) prefer to communicate with health professionals about the status of their sexual health through computer-mediated communication devices, 303 second-year university students (183 from an urban North American university and 120 from a periurban university in South Africa) completed a paper-based survey indicating how they prefer to communicate with doctors and nurses: talking face to face, mobile phone call, text message, Internet chat programs, Facebook, Twitter, or e-mail. Nearly all students, and female students in South Africa in particular, prefer to receive their STI test results, post-results counseling, and STI-related information by talking face to face with doctors and nurses rather than communicating through computers or mobile phones. Results are clarified in relation to gender, availability of various technologies, and prevalence of HIV in Canada and in South Africa.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".