Making Contributions and Defining Success: An eDelphi Study of the Inaugural Cohort of CIHR Health System Impact Fellows, Host Supervisors, and Academic Supervisors
Bibliographic record
Abstract
Context:The Health System Impact (HSI) Fellowship, an innovative training program developed by the Canadian Institutes of Health Research' s Institute of Health Services and Policy Research, provides PhD-trained health researchers with an embedded, experiential learning opportunity within a health system organization.Methods/Design: An electronic Delphi (eDelphi) study was conducted to: (1) identify the criteria used to define success in the program and (2) elucidate the main contributions fellows made to their organizations.Through an iterative, two-round eDelphi process, perspectives were elicited from three stakeholder groups in the inaugural cohort of the HSI Fellowship: HSI fellows, host supervisors and academic supervisors.Discussion: A consensus was reached on many criteria of success for an embedded research fellowship and on several perceived contributions of the fellows to their host organization and academic institutions.This work begins to identify specific criteria for success in the fellowship that can be used to improve future iterations of the program. RésuméContexte : Les bourses d' apprentissage en matière d'impact sur le système de santé (BAIS) -un programme de formation novateur mis au point par l'Institut des services et des politiques de santé des Instituts de recherche en santé du Canada -offrent aux chercheurs titulaires d' un Marc-André Blanchette et al.
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 | 0.038 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".