Applying a ‘stages of change’ model to enhance a traditional evaluation of a research transfer course
Bibliographic record
Abstract
The aim of this study was to utilize an evaluation tool based on Prochaska's model of change in order to assess behaviour change as part of an evaluation process for a research transfer training programme (RTTP). The RTTP was a training programme offered to scientists in a psychiatry department and research institute to gain skills in research transfer. In addition to a traditional course evaluation framework evaluating overall satisfaction with the course and whether or not learning objectives were met, an additional 'stages of change' evaluation tool designed to assess change along a continuum was utilized. This instrument measured change in participants' attitudes, intentions and actions with respect to research transfer practice and consisted of a 12-question survey completed by participants prior to taking the course and 3 months post-course. In two out of the three categories, attitudes and intention to practice, there was positive change from pre- to post-course (P < 0.05). Although there was a trend of increased RT-related action, this was less robust and did not reach significance. For the RTTP transfer course, a 'stages of change' model of evaluation provided an enhanced understanding by showing changes in participants that would otherwise have been overlooked if only changes in RT behaviour were measured. Additionally, evaluating along a change continuum specifically identifies areas for improvement in future courses. The instrument developed for this study could also be used as a pre-course, participant needs assessment to tailor a course to the change needs of participants. Finally, this 'stages of change' approach provides insight into where barriers to change may exist for research transfer action.
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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 | 0.120 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".