Ensuring Best Practice in Genomic Education and Evaluation: A Program Logic Approach
Bibliographic record
Abstract
Targeted genomic education and training of professionals have been identified as core components of strategies and implementation plans for the use of genomics in health care systems. Education needs to be effective and support the sustained and appropriate use of genomics in health care. Evaluation of education programs to identify effectiveness is challenging. Furthermore, those responsible for development and delivery are not necessarily trained in education and/or evaluation. Program logic models have been used to support the development and evaluation of education programs by articulating a logical explanation as to how a program intends to produce the desired outcomes. These are highly relevant to genomic education programs, but do not appear to have been widely used to date. To assist those developing and evaluating genomic education programs, and as a first step towards enabling identification of effective genomic education approaches, we developed a consensus program logic model for genomic education. We drew on existing literature and a co-design process with 24 international genomic education and evaluation experts to develop the model. The general applicability of the model to the development of programs was tested by program convenors across four diverse settings. Conveners reported on the utility and relevance of the logic model across development, delivery and evaluation. As a whole, their feedback suggests that the model is flexible and adaptive across university award programs, competency development and continuing professional development activities. We discuss this program logic model as a potential best practice mechanism for developing genomic education, and to support development of an evaluation framework and consistent standards to evaluate and report genomic education program outcomes and impacts.
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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 | 0.543 | 0.402 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".