Participation in HIV Research: The Importance of Clinic Contact Factors
Bibliographic record
Abstract
Recruiting minority populations living with HIV to many types of clinic-based HIV research is a concern. This study examined an expanded range of predictors of HIV research participation (clinic contact, clinical, and personal characteristics) to investigate observed ethnocultural differences in HIV research participation. Research participation was defined as participation in any of diagnostic, pathogenesis, drug trial or survey research. Logistic regression modeling was used to predict research participation of 657 eligible patients (93% of the patient population) who began care between January 1997 and the end of September 2003 at a regional outpatient HIV care program in Calgary, Canada. Approximately one third (32%) were non-white, including 18% Aboriginal, 9% black, 4% Asian, and 1% Hispanic individuals. Twenty-nine percent (187/657) of the patients participated in at least one study of any kind. Multivariate analysis indicated that the strongest predictors of any research participation (including diagnostic, pathogenesis, drug trial, or survey studies) are clinical (including nadir CD4 count [odds ratio {OR} = 0.132, p < 0.05] and antiretroviral medication [OR = 3.041, p < 0.01]), and clinic contact characteristics (years in treatment [OR = 1.949, p < .05], and percentage of appointments kept [OR = 1.022, p < .05]). Few personal characteristics were related to research participation-only reported injection drug use (OR = 0.363, p < 0.05), and birth year (OR = 0.968, p = 0.054) were significantly or marginally related to research participation. There was no significant difference among ethnocultural groups (white, non-white). Results thus suggest that much of the variation in research participation associated with ethnocultural group or race is accounted for by clinic contact variables. This in turn suggests that it is the different patterns of clinical interactions and service use shown by these groups that may influence research participation. To attract under researched populations, attention should shift from the "who" of research participation to the "how" of clinical interactions.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".