Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a growing interest in using public data for open government policy involving health informatics and healthcare systems. This paper investigated the characteristics of publically available data sets in health informatics that were derived from electronic health records (EHRs), healthcare systems, and a variety of open-government libraries, data marts, or data catalogues.Data used in this study consisted of public data sets that did not require any registration to access online. In total, nine web-based platforms on the Internet were used that included: British Columbia (BC) Data Catalogue, Canadian Institute for Health Information (CIHI), Harvard Dataverse, MIMIC-eICU, FigShare, GitHub, Google Dataset, UCI Machine Learning Repository, and Zenodo. Our initial search across these platforms found over 10,000 public use files that had data sets related to health informatics.We found 558 data sets that matched search criterion that ranged from years 2013-2022. The data source types were mostly found using the health informatics search filters followed by the combination of health informatics and healthcare systems, but fewer data sets were found when using EHR as the criterion. Almost 85% of the total data sets were from 2020-2022. The range of data sizes were 11KB to 7.8MB. The eICU (hosted by MIT’s MIMIC data mart) platform had the largest data set followed by Zenodo, and GitHub. Additionally, any bioinformatics in the 558 data sets were excluded and further classification on the content and usability, and dashboard visualization towards experiential learning resulted in 117 data sets.Of these 117 data sets, we further tested their usability to graph and create a dashboard within 2-5 minutes of loading the data to Tableau© that then used a Data Usability Scale (DUS) scoring developed from the industry standard of System Usability Scale (SUS). Data were deemed usable and useful for >60% average DUS scoring. Finally, 25 sets of data could be used effectively in classroom exercises dealing with electronic records and decision support for health care. Best data for dashboard usability were from MIMIC-eICU, and other websites like Zenodo produced low to high usability. The data sets with low to poor usability were from FigShare, Dataverse, CIHI, and BC Data Catalogue, respectively.Overall, 25 data sets with high usability of data related health informatics and healthcare systems showed 60-85% usability. Moreover, all nine platforms showed ease-of-use search patterns to establish the criteria in a short amount of time. However, more investigation is needed to compare data-to-dashboard visualization for single to multiple files for experiential learning in health informatics.
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.
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 | 0.023 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.039 | 0.035 |
| Research integrity | 0.000 | 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 it