Implementing a Simpler Approach to Mission-Based Planning in a Medical School
Bibliographic record
Abstract
Changes in the education, research, and health care environments have had a major impact on the way in which medical schools fulfill their missions, and mission-based management approaches have been suggested to link the financial information of mission costs and revenues with measures of mission activity and productivity. The authors describe a simpler system, termed Mission-Aligned Planning (MAP™), and its development and implementation, during fiscal years 2002 and 2003, at the School of Medicine at the University of Texas Health Science Center at San Antonio, Texas. The MAP system merges financial measures and activity measures to allow a broad understanding of the mission activities, to facilitate strategic planning at the school and departmental levels. During the two fiscal years mentioned above, faculty of the school of medicine reported their annual hours spent in the four missions of teaching, research, clinical care, and administration and service in a survey designed by the faculty. A financial profit or loss in each mission was determined for each department by allocation of all departmental expenses and revenues to each mission. Faculty expenses (and related expenses) were allocated to the missions based on the percentage of faculty effort in each mission. This information was correlated with objective measures of mission activities. The assessment of activity allowed a better understanding of the real costs of mission activities by linking salary costs, assumed to be related to faculty time, to the missions. This was a basis for strategic planning and for allocation of institutional resources.
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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".