Developing a reporting item checklist for studies of HIV drug resistance prevalence or incidence: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Adequate surveillance of HIV drug resistance prevalence is challenged by heterogenous and inadequate data reporting. To address this issue, we recently published reporting guidance documentation for studies of HIV drug resistance prevalence and incidence. OBJECTIVES: In this study, we describe the methods used to develop this reporting guidance. DESIGN: We used a mixed-methods sequential explanatory design involving authors and users of studies of HIV drug resistance prevalence. In the quantitative phase, we conducted a cross-sectional electronic survey (n=51). Survey participants rated various reporting items on whether they are essential to report. Validity ratios were computed to determine the items to discuss in the qualitative phase. In the qualitative phase, two focus group discussions (n=9 in total) discussed this draft item checklist, providing a justification and examples for each item. We conducted a descriptive qualitative analysis of the group discussions to identify emergent themes regarding the qualities of an essential reporting item. RESULTS: We identified 38 potential reporting items that better characterise the study participants, improve the interpretability of study results and clarify the methods used for HIV resistance testing. These items were synthesised to create the reporting item checklist. Qualitative insights formed the basis of the explanation, elaboration, and rationale components of the guidance document. CONCLUSIONS: We generated a list of reporting items for studies on the incidence or prevalence of HIV drug resistance along with an explanation of why researchers believe these items are important. Mixed methods allowed for the simultaneous generation and integration of the item list and qualitative insights. The integrated findings were then further developed to become the subsequently published reporting guidance.
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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 | 0.512 | 0.560 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".