How Big Should My Dot Be? Using Spreadsheet Simulation to Evaluate Process Improvement Data Collection Strategies
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
Occasionally, healthcare professionals approach analytical topics and courses with a blend of fear and loathing. They may fail to grasp the connection between particular principles and their application to actual process analysis and improvement. The run chart is a persuasively powerful quantitative tool. Healthcare teams could use this tool to better comprehend the extent of process changes over time. Although constructing the run chart is mathematically simple, healthcare professionals may be uncertain as to sample size sufficiency. They may also be unsure of the number of observations required in run chart subgroups. We developed a spreadsheet simulation model to provide enhanced relevance for the topic of run chart subgroup size determination. This classroom-tested active learning exercise helps healthcare teams to visually understand the relationships between subgroup size, underlying process variation, and anticipated levels of improvement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.000 |
| 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 it