Mapping Health on the Internet
Bibliographic record
Abstract
This thesis is situated in the converging fields of geographic information systems (GIS), multimedia applications on the World Wide Web, and public health surveillance. Recent technological advances in multimedia and GIS software have enabled public health organizations to distribute health maps over the Web, potentially allowing remote users to perform queries and analytical operations. Both static maps and interactive analysis may improve public health through targeted health surveillance and subsequent interventions. The thesis reviews privacy, technological, and cartographic issues related to distributing health data over the Web and evaluates 30 existing sites, allowing for a representative sample of the current state of Web health mapping and suggestions for future health-GIS sites. Most of the sites reviewed were technically sound, but offered little or no opportunity for users to design maps and carry out analyses. The few interactive sites now online allowed for simple queries. Based on ongoing research, we will demonstrate a prototype site that allows for increased interactive use by remote users. Research shows public health officials are concerned about costs involved in incorporating new technology, such as GIS, into their work. The prototype website was created using free software to minimize costs and focuses on asthma, socioeconomic and air pollution data from Hamilton, Ontario. Upon completion the prototype site was tested by target users during a focus group at McMaster University. Survey results from the focus group reiterate findings from the literature. The majority of respondents are interested in incorporating GIS, openGIS and spatial data, but they are concerned of the costs involved with new software. Complete results from the survey administered and how the results led to changes in the original prototype are documented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".