Academic health leadership: looking to the future. Proceedings of a workshop held at the Canadian Institute of Academic Medicine meeting Québec, Que., Canada, Apr. 25 and 26, 2003.
Bibliographic record
Abstract
The academic health sector will face major changes in governance, health care delivery, educational requirements and research programs over the next decade. Increased emphasis on disease prevention and health outcomes, the need for evidence to support both clinical and policy decisions, educational changes both in content and delivery, and the importance of working in teams will challenge the academic health care community. Large research teams may require new ways of training and nurturing young investigators, including improved grant writing and knowledge translation, human resource management skills and the ability to interact with disciplines that have different research methodologies. MD/PhD and Clinician Investigator Programs may help to fill these gaps in medicine, but nursing is faced with a serious shortage of doctoral-trained educators and researchers and may need targeted programs to achieve a critical mass of academics able to accept leadership roles. The success of the Quebec model of support for health research networks and researchers is encouraging. There is a leadership gap within health care institutions that spans jurisdictions and affects both institutional performance and individual careers. Young investigators need good mentors and adequate protected time to acquire the skills necessary for leadership roles. Policy changes within health care institutions and academic organizations will be necessary to adapt to the coming decade. The Canadian Institute of Academic Medicine is committed to developing better mentoring strategies for the next generation of academic leaders and to creating formal assessments of major Canadian health issues that can be used by health care advocacy groups when talking with policy-makers.
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 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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".