Communicating about risk for sexually transmitted HIV on the front lines in Ontario, Canada
Bibliographic record
Abstract
In Ontario, front-line HIV workers play a critical role in knowledge translation and are an instrumental link between research literature and the people most at risk for HIV. However, the way people use and interact with HIV information is influenced by a number of factors. The current study explored the challenges that Ontario front-line HIV workers face and their successful strategies, in order to better understand how to help those at risk access and use critical HIV information. Focus groups were conducted with Ontario front-line HIV workers (n = 20). A follow-up survey was used to confirm the emergent themes with a larger sample of front-line workers (n = 51) and to collect additional information. A number of important themes emerged from these analyses. One of the biggest challenges reported by front-line workers was regarding statistical and other numeric data related to HIV risk. Many front-line workers were not comfortable discussing numeric information in their work because either they felt people would not understand, or they themselves had difficulty. The survey data confirmed this theme and provided additional information about how front-line workers best understand statistics as well as their preferred format for HIV risk information. The findings of the current study offer insights into the strengths and challenges front-line HIV workers experience in their work with those at-risk for sexually transmitted HIV. Importantly, these findings provide valuable information about how knowledge producers (e.g., researchers) and providers (e.g., federal and provincial HIV organizations) can better tailor the latest information about HIV to more effectively support front-line workers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".