P068: Developing a standardized knowledge dissemination tool for communicating the need for Choosing Wisely© in Alberta’s emergency departments
Bibliographic record
Abstract
Introduction: Standardized tools for disseminating knowledge summaries of low value or unnecessary care (e.g., testing, procedures and treatments) are limited, but needed to equip clinicians for discussions with patients about care decisions. The objective of this study is to assess the acceptability of a tool developed by our emergency department (ED) team to communicate the evidence supporting the Choosing Wisely Canada© (CWC) and other similar recommendations. Methods: A consensus process was used by team members to develop a tool that highlights three areas: Facts, Gaps, and Acts. The Facts portion highlights the current state of knowledge and illustrates the strength of the evidence supporting guideline recommendations. The Gaps section identifies variation in current clinical practice. The Acts section includes larger CWC goals, as well as specific next steps for a demonstration project. Each section contains one key message for clinicians, ensuring the tool is easy to use. Results: A test case has been developed for avoiding chest radiographs in patients with an exacerbation of documented asthma. The Facts section reviewed current guidelines for asthma care. The Gaps section collated evidence from a systematic review and primary research. The Acts section recapitulates the CWC recommendations. In order to assess acceptability feedback cycle will be completed using surveys of 50 patients and 50 clinicians. Conclusion: While generating the Facts, Gaps, and Acts tool for a CWC recommendation represents a translational activity, evidence of effectiveness is needed prior to widespread implementation. We report the rational and development of a novel tool to engage clinicians and patients in conversations about unnecessary care in the ED.
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 | 0.160 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".