Towards a Human-Centered Approach to Data Science in Healthcare: An Exploration of Methods
Bibliographic record
Abstract
Data science has become pervasive in healthcare, where it has both the potential to improve health outcomes and the risk of leaving some people on the sidelines. In this thesis, we adopt human-centered data science as a theoretical lens to explore how context and granularity in data can be leveraged in healthcare research. Through three exploratory studies, we demonstrate how categorization methods can add a nuanced layer to results and interpretations, thereby advancing our understanding of complex healthcare issues. Specifically, we use demographic segmentation, persona development and unsupervised clustering to nuance pre-existing assumptions, with the objective of supporting personalization of care. This thesis contributes to human-centered data science methodology and its application in healthcare, presenting significant opportunities for future work.
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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 | 0.184 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".