Evaluating a Motivational Interviewing Workshop for Medical Students at Queen’s University
Bibliographic record
Abstract
Motivational interviewing (MI), a partnership-based counselling style, has increasingly been used to promote behaviour change among adults. Despite its potential to support behaviour change, there is a lack of research examining the most effective ways to train health care practitioners to use MI when working with patients. The purpose of this research was to evaluate uptake of MI skills following a one-day MI workshop that was developed in partnership with Exercise is Medicine for second year medical students enrolled at Queen’s University. The workshop focused on developing a basic understanding of the MI spirit, the phases of MI, and the ways in which MI may be used in different behavioural contexts. Participants (n=69) were asked to complete a pre and post-questionnaire that explored their knowledge of MI and their current perceptions of MI in addition to completing the Helpful Responses Questionnaire (HRQ), a validated MI assessment tool. A process evaluation was also conducted for each facilitator to measure fidelity to MI during the workshop. Results from the process and outcome evaluations are currently being evaluated. Results from this study will help determine whether a one-day workshop format is an appropriate delivery method for teaching MI techniques. Future research is needed to determine if medical students utilize information gained in their medical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".