Strategic Planning in an Academic Radiation Medicine Program
Bibliographic record
Abstract
BACKGROUND: In this paper, we report on the process of strategic planning in the Radiation Medicine Program (rmp) at the Princess Margaret Cancer Centre. The rmp conducted a strategic planning exercise to ensure that program priorities reflect the current health care environment, enable nimble responses to the increasing burden of cancer, and guide program operations until 2020. METHODS: Data collection was guided by a project charter that outlined the project goal and the roles and responsibilities of all participants. The process was managed by a multidisciplinary steering committee under the guidance of an external consultant and consisted of reviewing strategic planning documents from close collaborators and institutional partners, conducting interviews with key stakeholders, deploying a program-wide survey, facilitating an anonymous and confidential e-mail feedback box, and collecting information from group deliberations. RESULTS: was established to guide programmatic pursuits during the ensuing 5 years, and an implementation plan was developed to guide the first year of operations. CONCLUSIONS: The strategic planning process provided an opportunity to mobilize staff talents and identify environmental opportunities, and helped to enable more effective use of resources in a rapidly changing health care environment. The process was valuable in allowing staff to consider and discuss the future, and in identifying strategic issues of the greatest importance to the program. Academic programs with similar mandates might find our report useful in guiding similar processes in their own organizations.
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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 | 0.042 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".