Developing a comprehensive database with sensitive health information: A profile of people living with HIV in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
INTRODUCTION: Developing a comprehensive cohort of people living with HIV (PLHIV) to help improve healthcare has long been the vision of researchers, clinicians and decision makers. The development of this kind of database is challenging and requires strict adherence to privacy and confidentiality policies. We explored procedures, activities and events in database development. OBJECTIVES: To understand processes of developing a database with sensitive health information in Newfoundland and Labrador (NL), and to investigate procedures and activities to develop the database within its environmental context. METHODS: A narrative case study was used to explain the challenges and procedures involved in developing a database for our population. The development of the PLHIV cohort in NL is provided as an example to demonstrate the complexity of the process. We linked three datasets that included patient-level data for PLHIV: 1. laboratory data; 2. HIV clinic data; 3. health administrative data, which allowed for the creation of a large database containing many variables describing the PLHIV cohort in the province. RESULTS: We developed a de-identified cohort of 251 PLHIV that contained 178 variables. Our case study showed database development is an iterative process. The main challenges were ensuring patient privacy and data confidentiality are not compromised and working with multi-custodian data. These challenges were addressed by establishing a data governance team. CONCLUSIONS: It is important that policy be implemented to merge siloed data sources in order to provide researchers with accurate and complete data that is required to conduct sound and precise research with maximum benefits for treatment and policy-making to improve health outcomes.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| 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 | 0.000 |
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".