ENABLING KNOWLEDGE TRANSLATION THROUGH THE CANADIAN DEPRESCRIBING NETWORK
Bibliographic record
Abstract
In Canada 66% of people aged ≥65 years take five or more medications per day, and 1-in-3 consumes a Beers List inappropriate prescription. The Canadian Deprescribing Network was launched in 2016 to mobilize older adults, clinicians, health care organizations and policy-makers to reduce inappropriate prescriptions by 50% over the next 3–5 years. The questions “who needs to do what, when, and with whom?” and “how should we communicate our messages?” drive our knowledge translation strategy. Building on system and individual-level levers for increasing capability, opportunity, and motivation to deprescribe, we struck sub-committees and developed an action plan to influence and achieve buy-in from the different target audiences in a distributed leadership fashion. A Deprescribing Fair was set-up to showcase deprescribing tools and methods being used across Canada. Media attention, the launch of the deprescribing.org website, promoting deprescribing champions within organizations, and disseminating newsletters has led to collaborative engagement in deprescribing.
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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".