Correction: Using participatory design to develop (public) health decision support systems through GIS
Bibliographic record
Abstract
BACKGROUND: Organizations that collect substantial data for decision-making purposes are often characterized as being 'data rich' but 'information poor'. Maps and mapping tools can be very useful for research transfer in converting locally collected data into information. Challenges involved in incorporating GIS applications into the decision-making process within the non-profit (public) health sector include a lack of financial resources for software acquisition and training for non-specialists to use such tools. This on-going project has two primary phases. This paper critically reflects on Phase 1: the participatory design (PD) process of developing a collaborative web-based GIS tool. METHODS: A case study design is being used whereby the case is defined as the data analyst and manager dyad (a two person team) in selected Ontario Early Year Centres (OEYCs). Multiple cases are used to support the reliability of findings. With nine producer/user pair participants, the goal in Phase 1 was to identify barriers to map production, and through the participatory design process, develop a web-based GIS tool suited for data analysts and their managers. This study has been guided by the Ottawa Model of Research Use (OMRU) conceptual framework. RESULTS: Due to wide variations in OEYC structures, only some data analysts used mapping software and there was no consistency or standardization in the software being used. Consequently, very little sharing of maps and data occurred among data analysts. Using PD, this project developed a web-based mapping tool (EYEMAP) that was easy to use, protected proprietary data, and permit limited and controlled sharing between participants. By providing data analysts with training on its use, the project also ensured that data analysts would not break cartographic conventions (e.g. using a chloropleth map for count data). Interoperability was built into the web-based solution; that is, EYEMAP can read many different standard mapping file formats (e.g. ESRI, MapInfo, CSV). DISCUSSION: Based on the evaluation of Phase 1, the PD process has served both as a facilitator and a barrier. In terms of successes, the PD process identified two key components that are important to users: increased data/map sharing functionality and interoperability. Some of the challenges affected developers and users; both individually and as a collective. From a development perspective, this project experienced difficulties in obtaining personnel skilled in web application development and GIS. For users, some data sharing barriers are beyond what a technological tool can address (e.g. third party data). Lastly, the PD process occurs in real time; both a strength and a limitation. Programmatic changes at the provincial level and staff turnover at the organizational level made it difficult to maintain buy-in as participants changed over time. The impacts of these successes and challenges will be evaluated more concretely at the end of Phase 2. CONCLUSION: PD approaches, by their very nature, encourage buy-in to the development process, better addresses user-needs, and creates a sense of user-investment and ownership.
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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 | 0.037 | 0.474 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.080 | 0.030 |
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".