Using Exploratory Focus Groups to Establish a Sampling Strategy to Investigate Disability Experienced by Adults Living with HIV
Bibliographic record
Abstract
In HIV clinical research, participants are typically sampled based on demographic and/or disease characteristics. As little is known about HIV-specific disability, we did not know whether this purposive type of sampling would be helpful and what characteristics (if any) should guide our sampling strategy. We describe using exploratory focus groups to determine a sampling strategy to investigate disability from the perspective of adults living with HIV. We conducted 4 focus groups with 23 men and women and asked participants to describe their health-related challenges and impact on their overall health. We analyzed data to determine whether health-related challenges differed based on age, gender, ethnocultural background, length of time since HIV diagnosis and antiretroviral use and if these characteristics should be considered when sampling. Participants described seven health-related challenges that appeared not to vary based on demographic or disease characteristics. Variations emerged in the way health-related challenges manifested and the strategies participants used to deal with these challenges. Consequently, we decided upon a broad theoretical sampling strategy for the subsequent interview phase. Exploratory focus groups may be a useful technique to determine a sampling strategy when exploring a new phenomenon in HIV qualitative research.
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 | 0.118 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".