Development of training for medicines-oriented policymakers to apply evidence
Bibliographic record
Abstract
BACKGROUND: Health systems globally promote appropriate prescribing by healthcare providers and safe and effective medicine use by consumers. Rx for Change, a publicly available database, provides access to systematic reviews regarding best practices for prescribing and using medicines. Despite the value of the database for improving prescribing and medicine use, its use remains suboptimal. This study aimed to develop a training program for five medicine-focused organisations in Canada and Australia to facilitate the use and understanding of the Rx for Change database. METHODS: Four steps were undertaken: 1) key informant interviews were completed across all organisations to understand the knowledge user perspective; 2) a directed content analysis was completed of the interview transcripts and proposed training was developed; 3) a second round of feedback on the proposed training by knowledge users was gathered; and 4) feedback was integrated to develop the final training. RESULTS: Sixteen key informant interviews with knowledge users were conducted. Themes for training content included the scope of, navigation and strategies for using Rx for Change (generic content) and practical examples on incorporating evidence within their workplace context (tailored content). The final training consisted of an informational video, a 60-minute face-to-face workshop and two post-training reminders. CONCLUSIONS: A method of engaging knowledge users in the development of a training program to improve the use of an on-line database of systematic reviews was established and used to design training. Next steps include the delivery and evaluation of the training.
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 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 | 0.038 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads 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".