Evaluation of a training program for medicines-oriented policymakers to use a database of systematic reviews
Bibliographic record
Abstract
Background: Suboptimal prescribing and medications use is a problem for health systems globally. Systematic reviews are a comprehensive resource that can help guide evidence-informed decision-making and implementation of interventions addressing such issues; however, a barrier to the use of systematic reviews is their inaccessibility (due to both dispersion across journals and inaccessibility of content). Publicly available databases, such as Rx for Change, provide quick access to summaries of appraised systematic reviews of professional and consumer-oriented interventions to improve prescribing behaviour and appropriate medication use, and may help maximise the use of evidence to inform decisions. The present study aims to evaluate a training program to improve attitudes towards, confidence in skills, intentions to use, and use of systematic review evidence contained within Rx for Change. Methods: Guided by the Knowledge to Action framework, a training program with content customised to local provider and consumer contexts was developed with knowledge user input. The training program consisted of a 6 minute information video, a 1 hour workshop with hands-on, interactive and didactic components, and two post-training reminders. Forty-nine people from five medicines-focused organisations in Canada and Australia attended one of six workshops. Participants were surveyed immediately pre and post and 3 months after training to evaluate their attitudes towards, confidence in skills, intentions to use, and use of Rx for Change, and attitudes towards and confidence in skills for using evidence for decision-making. Analyses for differences for each of the outcomes at three time points (pre, post and 3 months after training) was performed using a random effects model. Results: Immediately post-training, there were higher respondent attitudes towards Rx for Change (mean increase = 0.54 out of 5, 95% CI, 0.18-0.83, P < 0.005); intention to use Rx for Change (0.53, 95% CI, 0.21-0.86, P < 0.005); confidence in skills for using Rx for Change (2.08, 95% CI, 1.74-2.42, P < 0.005); and confidence in skills for using evidence in policy decision-making (0.50, 95% CI, 0.22-0.77, P < .005) compared to pre-training. Confidence in skills for using both Rx for Change and evidence were maintained 3 months after training (both P < 0.005). Conclusions: Participants of this training program reported sustained improvements in their confidence in skills for using evidence in policy decision-making. This may have important implications for uptake of systematic review evidence promoting improved prescribing and medication use.
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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 | 0.192 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier 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".