Using Qualitative Methods to Assess the Measurement Property of a New HIV Disability Questionnaire
Bibliographic record
Abstract
The purpose of this article is to describe our experience using a qualitative team approach and predetermined theoretical framework to assess sensibility of a newly developed HIV disability questionnaire. Two interviewers conducted structured qualitative interviews with 22 adults living with HIV, asking participants how well the questionnaire characterized the disability they experienced living with HIV. Data collection and analysis occurred over six stages with four analysts who met throughout. Strengths of our approach included the ability to assess the sensibility of the questionnaire from the perspective of adults living with HIV, collect and analyze data across multiple sites, establish a systematic team analytical process, and enhance rigour through multiple coding, team reflexivity, and interviewer and analyst triangulation. Challenges included increased resources required to coordinate and implement this approach, differential recruitment rates, initial divergent analytical styles, and the potential to miss emerging codes given the structured nature of the analysis. This article offers a methodological process for researchers to use a qualitative team approach with directed content analysis to assess the sensibility of a new health status questionnaire.
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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 | 0.136 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".