Developing Competencies for Health System Impact: Early Lessons learned from the Health System Impact Fellows
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.
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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".