Theories, Models, and Frameworks (TMFs) to Assess Respiratory Health Equity of Big Data; A Scoping Review
Bibliographic record
Abstract
In the last few decades, big data has been considered a useful tool in reducing health inequities due to its innate high volume of data on the general population and the numerous sources recording the data (Ibrahim et al., 2020; Wesson et al., 2022). Health equity, the state in which every person has a fair opportunity to attain their highest health potential is an ongoing goal for the public health system. Health equity-based analyses of big data allow researchers, decision-makers, and policymakers to identify differences between population groups including healthcare accessibility and health outcomes. Although big data has immense potential to address health disparities, several publications have cautioned that health equity analyses using big data can worsen the inequities when the reasons behind the patterns in the data are not assessed, especially in underrepresented populations (Househ et al., 2017; Veinot et al., 2018; Wesson et al., 2022). Considering the risks associated with big data, it is necessary for researchers to recognize biases present in big data creation and collection when generating and interpreting the results of health equity-based analyses. Currently, there is no consensus on how to assess health equity in big datasets, and several approaches have been proposed (Nsoesie & Galea, 2022; Sabet et al., 2023). However, there is a growing need to evaluate the characteristics and sources of big data to determine its appropriateness for health equity research. Therefore, the aim of this scoping review is to identify and explore the existing theories, models, and frameworks proposed for assessing the degree to which big data is equity informed. In addition, the potential utility of these frameworks will be assessed in the Canadian context.
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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 | 0.063 | 0.182 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.033 | 0.026 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".