Going Beyond Kirkpatrick in Evaluating a Clinician Scientist Program: Itʼs Not “If It Works” but “How It Works”
Bibliographic record
Abstract
PURPOSE: To explore how the Canadian Child Health Clinician Scientist Program (CCHCSP) works to achieve prearticulated and emergent outcomes. METHOD: In 2009, after gaining ethical approval from the Hospital for Sick Children, the authors examined quantitative data (e.g., participation in curriculum elements) to ensure sufficient exposure by trainees to the program and quantitative outputs (e.g., publications) to measure achievement of CCHCSP goals. They identified emergent outcomes through grouping and analyzing qualitative data generated through interviews with program graduates. Then, to explore possible theoretical explanations for the emergent findings, the authors conducted a literature review. RESULTS: Graduates participated in high rates in each component of the CCHCSP and produced publications, presented research, and received funding. Interview data revealed an unexpected outcome: that the CCHCSP helped graduates to form new professional identities. These data, along with theoretical assumptions from Ibarra's theory on professional identity change, informed a new theory or model for the CCHCSP. CONCLUSIONS: Early investment in building a program's logic model is invaluable for understanding program goals and for guiding program planning and development. Both employing a strategy that captures emergent program outcomes and investigating (e.g., through a literature search) why and how the program actually works to arrive at these outcomes informs the development and evaluation of future program offerings and may, as in the case of the CCHCSP, offer a new program model or theory.
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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 | 0.158 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".