Identifying User Needs for Designing Data-based Public Health Decision Making Tools (Preprint)
Bibliographic record
Abstract
BACKGROUND Public health professionals regularly engage in complex tasks involving data and models on large population segments, and use various tools to help support their data-driven decision-making. Human factors methods can be employed in the design of such tools to better support public health professionals in decision-making tasks. OBJECTIVE While human factors methods have been applied to the design of some individually-based clinical health tools, their applications are limited in the design of public health systems. We conducted focus groups with public health professionals in Ontario, Canada to understand their informational needs in decision support tools to support a human factors approach to designing data-based tools for public health. METHODS Three focus groups were completed, each consisting of a group of four participants. The discussion included a critique of pre-existing public health tools and their interfaces, a discussion of decision support tools and population risk tools familiar to the participants, and sought out insights on developing a chronic disease prediction dashboard as a use case. RESULTS Data was recorded, transcribed, and coded by three independent reviewers to extract themes using affinity diagramming. We present system features identified by public health professionals, and provide an example of how to use and prioritize these suggested features by applying them to our use case. CONCLUSIONS Focus groups revealed that users want a tool that identifies the end user early in the design process, has quality visualizations, provides transparency in data inputs and outputs, and incorporates equity considerations. This research provides a critical perspective on the potential for human factors to improve the use of data and analytics for public health applications.
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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 | 0.072 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".