Visualizing survey data: disseminating results from a population health survey on HIV and AIDS in Canada
Bibliographic record
Abstract
Effective knowledge dissemination of population survey results benefit the end user when results are engaging and visually appealing, as they enhance understanding and move research into action. In 2011, the CIHR Social Research Centre in HIV Prevention (SRC) at the University of Toronto's Dalla Lana School of Public Health and the Canadian Foundation for AIDS Research (CANFAR) conducted a national population health survey to gain a better understanding of Canadians' behaviours, attitudes, knowledge and perceptions of HIV and AIDS. To maximize the dissemination of these survey results, the team was funded by a Canadian Institutes of Health Research (CIHR) grant to build a prototype for an open source (or open access or non proprietorial) web-based data visualization tool. The interactive tool visualizes the survey data using both spatial and non-spatial elements and utilizes both Drupal and Google map and charts scripts. The tool is currently undergoing evaluation by its target knowledge users, which are staff at organizations that provide HIV and AIDS-related services across Canada. Future plans are to further build-out non-spatial visualization components as well as add additional data to the platform. This project involves a multi-disciplinary collaboration between public health researchers, geographers, librarians and professionals from community-based AIDS organizations. This session will describe the process of developing the data visualization tool; share the results from the evaluation data collected ; and discuss the challenge of designing a tool that engages users through an easily accessible and visually pleasing representation without losing the multidimensional complexity of the data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".