Implementation of a multi-level evaluation strategy: a case study on a program for international medical graduates
Bibliographic record
Abstract
Evaluation of educational interventions is often focused on immediate and/or short-term metrics associated with knowledge and/or skills acquisition. We developed an educational intervention to support international medical graduates working in rural Victoria. We wanted an evaluation strategy that included participants' reactions and considered transfer of learning to the workplace and retention of learning. However, with participants in distributed locations and limited program resources, this was likely to prove challenging. Elsewhere, we have reported the outcomes of this evaluation. In this educational development report, we describe our evaluation strategy as a case study, its underpinning theoretical framework, the strategy, and its benefits and challenges. The strategy sought to address issues of program structure, process, and outcomes. We used a modified version of Kirkpatrick's model as a framework to map our evaluation of participants' experiences, acquisition of knowledge and skills, and their application in the workplace. The predominant benefit was that most of the evaluation instruments allowed for personalization of the program. The baseline instruments provided a broad view of participants' expectations, needs, and current perspective on their role. Immediate evaluation instruments allowed ongoing tailoring of the program to meet learning needs. Intermediate evaluations facilitated insight on the transfer of learning. The principal challenge related to the resource intensive nature of the evaluation strategy. A dedicated program administrator was required to manage data collection. Although resource-intensive, we recommend baseline, immediate, and intermediate data collection points, with multi-source feedback being especially illuminating. We believe our experiences may be valuable to faculty involved in program evaluations.
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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 | 0.054 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| 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".