User Task Scenarios for Map-Based Decision Support in Community Health Planning
Bibliographic record
Abstract
Health outcomes are affected by the socio-demographic and physical-environmental characteristics of the places where people live. Therefore, epidemiologists have been interested in the use of maps to explore spatial patterns of disease for a long time. Geographic Information Systems (GIS) are not only useful when visualizing complex spatial datasets but also when mapping the results of analytical processes. One such process is multi-criteria evaluation (MCE), which can be used to generate composite measures of public health based on individual, medical and non-medical factors. The objective of this study was to determine if geovisual MCE can be an effective tool in community health planning. We provided highly interactive thematic maps coupled with MCE tools to planners at a community health centre and evaluated their use for community health planning and decision-making. User task scenarios were designed in a way to compare the usefulness of different representation methods for a number of tasks. The pilot user test with two expert participants included interviews, questionnaires, and user task scenarios with think-aloud audio and screen video recording. We assessed the easiness of completing the tasks using completion rates and times and could identify a number of specific usability issues with the tool at hand.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".