The role of theory-based outcome frameworks in program evaluation: Considering the case of contribution analysis
Bibliographic record
Abstract
In an era demanding greater accountability and the demonstration of positive outcomes and impacts, the field for the evaluation of interventions, program development and outreach projects is being challenged in many fields, including education, medical care, public health and social development. In consequence, the leaders in this field significantly changed their approaches to the evaluation of such interventions. Evaluators noted that simple linear models of evaluation do not address the wider community of interests and stakeholders involved in today's innovative and wide-reaching programs. Moreau raises the possible usefulness of contribution analysis in responding to the calls for broader accountability. In this commentary, the elements of these emerging approaches are reviewed and explained for teachers with reporting responsibilities in health sciences education. The presentation is intended to expand on Moreau's argument and suggestions such that educators may be able to consider the use of theory-based evaluations, such as contribution analyses, in the evaluation of their institutional programs and interventions. These possible applications are especially relevant to the increasingly more complicated and complex interventions that characterize many of the educational interventions as more health profession programs are moved into and impact on the larger societal community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".