A Needs Assessment Survey of Division Chiefs at an Academic Children’s Hospital
Why this work is in the frame
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Bibliographic record
Abstract
Purpose: The Division Chief at an academic health sciences centre has many leadership roles and responsibilities. There are no data on leadership training needs for Division Chiefs, and so we sought to design and implement a needs assessment for pediatric Division Chiefs at CHEO, a pediatric academic health sciences centre in Eastern Ontario, Canada. Methods: A needs assessment survey was developed with the aim to document demographics, preparedness for the role of Division Chief and desired leadership training for the role. This survey was piloted, revised and then distributed to all the Division Chiefs at our institution. The results of each question were collated, and simple descriptive statistics were calculated. Results: The survey was completed by 22 of 31 Division Chiefs. The majority of respondents were from the Department of Pediatrics (63.6%), followed by Surgery (20%), Psychiatry (3.3%) and Laboratory Medicine (3.3%). Their mean length of time as Division Chief was 5.5 years. Seventy-seven percent had concurrent leadership roles in addition to the role of Division Chief. None felt they were very well prepared for the role, five felt they were somewhat well prepared, nine were neutral, five were somewhat unprepared and three were very unprepared for the role. Half of the respondents received mentoring, either formal or informal, for their role and all but one felt that formal mentoring would have been useful. In terms of desired training, the Division Chiefs felt they had the most knowledge and skills in patient safety. All wanted training in developing divisional budgets, and many desired training in supporting the academic mission of the Division. Conclusion: Overall, this needs assessment identified an unmet need for leadership training and development among Division Chiefs. The findings are being used to optimize onboarding of Division Chiefs and an ongoing leadership development program targeted at this group.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it