Developing Competencies for Health System Impact: Early Lessons learned from the Health System Impact Fellows
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The Health System Impact (HSI) Fellowship program provides highly qualified post-doctoral fellows studying health services and policy research (HSPR) with opportunities for experiential learning, enriched core competency development and mentorship from senior-level leaders within health system organizations. Its overall aim is to prepare post-doctoral fellows with the research and professional skills, experiences and networks to make meaningful and impactful contributions in careers in academic and applied health system settings. OBJECTIVE: This study examined whether this HSI Fellowship program has contributed to the development of enriched core competencies in HSPR. METHODS: A competency assessment tool was developed and administered to the 46 fellows and their health system and academic supervisors from the inaugural HSI Fellowship cohort. Fellows' self-assessments at baseline, three months and 12 months were analyzed, along with supervisors' assessments at three and 12 months. Descriptive analyses were used to examine competency development over time. Differences by gender and between supervisor and fellow ratings were analyzed. RESULTS: HSI fellows' self-assessments indicate that they strengthened their skills in all 10 enriched core competencies. Supervisors' assessments of the fellows' competencies also improved from baseline to 12 months. Gender differences at baseline disappeared by the 12-month assessment. CONCLUSION: The HSI Fellowship provides an opportunity to develop the full suite of enriched core competencies, particularly in competency domains that are not currently emphasized in HSPR doctoral curriculum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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