An introduction to key performance indicators for medical physicists
Bibliographic record
Abstract
Qualified medical physicists (QMPs) are in a unique position to influence the creation and application of key performance indicators (KPIs) across diverse practices in health care. Developing KPIs requires the involvement of stakeholders in the area of interest. Fundamentally, KPIs should provide actionable information for the stakeholders using or viewing them. During development, it is important to strongly consider the underlying data collection for the KPI, making it automatic whenever possible. Once the KPI has been validated, it is important to setup a review cycle and be prepared to adjust the underlying data or action levels if the KPI is not performing as intended. Examples of specific KPIs for QMPs of common scopes of practice are provided to act as models to aid in implementation. KPIs are a useful tool for QMPs, regardless of the scope of practice or practice environment, to enhance the safety and quality of care being delivered.
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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.022 |
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".