Exploring organizational characteristics associated with practice changes following a mentored online educational module
Bibliographic record
Abstract
INTRODUCTION: Studies of health professionals' perceptions of barriers to and facilitators of research utilization in clinical practices suggest that structural and resource characteristics of service provider organizations are key determinants of the capacity of individual practitioners to provide evidence-based practices. In this pilot study, we compare health professionals' self-reported practice changes with characteristics of the structures and resources available to support research use at 4 hospitals. METHODS: Data on the self-reported practice changes of stroke rehabilitation professionals at Ontario hospitals were analyzed following their participation in a mentored online educational intervention, the Rehabilitation Education Program for Stroke (REPS). In-depth interviews with a purposefully drawn subsample of REPS mentors and managers of stroke rehabilitation programs examined the participating hospitals' structural and resource characteristics. The interview data on hospital characteristics were coded descriptively and thematically, quantified, and then compared with the percentage of individual REPS participants who reported positive practice changes in each hospital. RESULTS: Hospitals with higher percentages of participants reporting improved practices following REPS provided better computer access, paid time to participate in REPS, had established specialized units of stroke care, strong teamwork, and were previously committed to implementing best practices. They also conducted program audits or evaluations and engaged in "bottom-up" program decision making. DISCUSSION: Continuing educators should consider the capacity of hospitals to support practice changes when planning educational interventions for rehabilitation professionals. Larger studies employing objective measures are needed to examine relationships between practice improvements and organizational characteristics following educational interventions.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".