Development and evaluation of tailored, theory-informed training to support the implementation of an outcome measure: an explanatory sequential mixed method study
Bibliographic record
Abstract
PURPOSE: We aimed to describe the development of a tailored, theory-informed training session for an outcome measure (the Mayo-Portland Adaptability Inventory), and evaluate the session's impact on clinician reactions, learning, and behavioural intent. MATERIALS AND METHODS: We developed the training session using an integrated knowledge translation approach with stroke outpatient rehabilitation clinicians in Québec, Canada. We conducted a mixed-method explanatory sequential evaluation informed by the New World Kirkpatrick Model (reaction, learning, behavioural intent) composed of three surveys followed by interviews. We analyzed survey data using cumulative link mixed models, and interviews using directed content analysis. RESULTS: Eighty clinicians attended the training session, of which 51 responded to the surveys and 6 participated in interviews. Odds ratios indicate that individuals were more likely to rate themselves higher post-training on most outcomes. During the interviews, participants indicated that: they experienced positive reactions, learning and behavioural impacts from the session, negative attitudes and commitment were due to perceived limitations in the outcome measure, and training impacts were affected by contextual factors including a provincial mandate for the measure. CONCLUSION: Implementation teams could adapt this training design process to their context. Further research to understand how educational strategies work would produce more robust guidance.
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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 | 0.075 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".