Bibliographic record
Abstract
Graduate medical educators collect a vast amount of educational data about applicants, learners, faculty, programs, and systems. Using this data effectively can be difficult. Typically, we view our data in a table or Excel spreadsheet, draw a conclusion, and then take action. Yet optimizing the use of our data requires iterative, ongoing interaction with the stakeholders and data analysis skills. This iterative process helps us decide which features of the data are most relevant to represent visually and which visual presentation structure is best to communicate the data story.Data visualization involves translating information into a visual context such as a graph, chart, or map, to render the data easier to understand and to gain insights. Effective data visualization can quickly communicate large amounts of information and complex relationships, engage viewers, and facilitate opportunities to share insights and conclusions.1–3 In medical education, uses of data visualization range from assessment of resident competence in dashboards to communicating statistical findings from a research study.Often, data visualization is viewed as the last step in data analysis, used to present key findings rather than integrated as part of the data analysis process. Actively using data visualizations throughout the analysis process can prompt recognition of relationships between data points, additional questions, and new analyses. Data can be explored in many ways, such as descriptively (eg, plotting all points, mean, or median) and in more complex ways (eg, exploring the relationships between variables and reanalyzing data by subgroup), with the goal of identifying insights that meet the needs and objectives of the audience. Graphs, such as bar charts for categorical data and boxplots for continuous data, can inform preliminary visualizations.1,4,5 Once finalized, visual mockups are tested with the target audience(s) to ensure that the data addresses their needs and complements the compelling narrative within the written report. Interactive data representation, such as longitudinal competency dashboards, can meaningfully inform stakeholder objectives over time as well as the data analyst's understanding of the data.1,3
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 | 0.024 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.195 | 0.156 |
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".