Development of a Quality Assurance Score for the Nigeria AIDS Indicator and Impact Survey (NAIIS) Database: Validation Study
Bibliographic record
Abstract
BACKGROUND: In 2018, Nigeria implemented the world's largest HIV survey, the Nigeria AIDS Indicator and Impact Survey (NAIIS), with the overarching goal of obtaining more reliable metrics regarding the national scope of HIV epidemic control in Nigeria. OBJECTIVE: This study aimed to (1) describe the processes involved in the development of a new database evaluation tool (Database Quality Assurance Score [dQAS]) and (2) assess the application of the dQAS in the evaluation and validation of the NAIIS database. METHODS: The dQAS tool was created using an online, electronic Delphi (e-Delphi) methodology with the assistance of expert review panelists. Thematic categories were developed to form superordinate categories that grouped themes together. Subordinate categories were then created that decomposed themes for more specificity. A validation score using dQAS was employed to assess the technical performance of the NAIIS database. RESULTS: The finalized dQAS tool was composed of 34 items, with a total score of 81. The tool had 2 sections: validation item section, which contains 5 subsections, and quality assessment score section, with a score of "1" for "Yes" to indicate that the performance measure item was present and "0" for "No" to indicate that the measure was absent. There were also additional scaling scores ranging from "0" to a maximum of "4" depending on the measure. The NAIIS database achieved 78 out of the maximum total score of 81, yielding an overall technical performance score of 96.3%, which placed it in the highest category denoted as "Exceptional." CONCLUSIONS: This study showed the feasibility of remote internet-based collaboration for the development of dQAS-a tool to assess the validity of a locally created database infrastructure for a resource-limited setting. Using dQAS, the NAIIS database was found to be valid, reliable, and a valuable source of data for future population-based, HIV-related studies.
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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 | 0.160 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".