What’s Measured Is Not Necessarily What Matters: A Cautionary Story from Public Health
Bibliographic record
Abstract
A systematic review of the introduction and use of outcome-based performance management systems for public health organizations found differences between their use as a management system (which requires rigorous definition and measurement to allow comparison across organizational units) versus for improvement (which may require more flexibility). What is included in performance measurement/management systems is influenced by ease of measurement, data quality, ability of organization to control outcomes, ability to measure success in terms of doing things (rather than preventing things) and what is already happening. To the extent that most providers wish to do a good job, the availability of good data to enable benchmarking and improvement is an important step forward. However, to the extent that the health of a population is dependent on multiple factors, many beyond the mandate of the health system, too extensive a reliance on performance measurement may risk unintended consequences of marginalizing critical activities.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.009 |
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; both teacher heads agree on what is shown here.
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".