Towards a "Biography" of Population Health Data about Substance Use Disorders: Qualitative Approaches to Communicating Context
Bibliographic record
Abstract
BackgroundUnderstanding contexts of data production is necessary for using data accurately and ethically. “Biographies of data"" have developed as a complement to existing metadata as a way to critically examine and communicate social structures and power dynamics encoded in data. This project is focused on developing a “biography” of diagnostic codes, used to identify substance use disorders (SUD) in population health datasets. People with SUD regularly experience stigma and exclusion, particularly from healthcare settings. MethodsQualitative semi-structured interviews were conducted in 2024 with ""producers"" and “managers” of administrative data, including physicians and health information professionals, in British Columbia, Canada. The interviews explored processes involved with coding and billing, and the meaning of diagnostic codes from the perspective of those involved. This includes considerations of the population, social, and geographic factors, and implications of coding as inputs to decision-making. Reflexive thematic analysis was used to identify broad themes and report findings. ResultsAlthough data collection and analysis is ongoing, emerging findings have revealed structural, institutional, and social-level processes that influence coding decisions, as well as potential implications on research, policy making, and knowledge production about SUD. ConclusionAddressing the complexities of SUD and the overdose crisis will require a variety of data sources and approaches, and linked population health and administrative data will continue to be one important resource. Through better understanding the contexts and processes that inform the production and management of SUD-related population data, we will shed light on the ethical and social implications of their use.
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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 | 0.156 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.019 | 0.045 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".