Lab Wisely in Canada: Enhancing and highlighting the role of all clinical laboratory professionals in reducing harm and waste
Bibliographic record
Abstract
Abstract Introduction/Objective Stewardship initiatives are a key strategy for addressing inappropriate utilization of clinical laboratory resources. These approaches require engagement of multiple types of stakeholders. Some professional groups are historically underrepresented, such as those who perform specimen collection, testing, and quality processes. A specific campaign is needed to engage these groups and highlight their expertise. Methods/Case Report We surveyed Medical Laboratory Technologists and Medical Laboratory Assistants to understand the barriers they face to participating in laboratory stewardship initiatives. These survey findings helped shape tools and resources that we created for new campaign called Lab Wisely. We also identified that one-third of existing Choosing Wisely Canada recommendations relate to laboratory testing. We categorized and tagged each recommendation to create a publicly-available searchable database which was placed on the campaign website (LabWisely.ca). Results (if a Case Study enter NA) NA Conclusion Laboratory testing is featured in a significant proportion of all Choosing Wisely Canada recommendations, supporting the idea that the clinical laboratory should be heavily involved in reducing medical overuse in healthcare. In our survey, we found that laboratory professionals face time and workload constraints, but feel a professional responsibility for ensuring appropriate resource use by all users. There was also a lack of ‘know- how’ around tangible ways to become involved. The Lab Wisely website has become a one-stop-shop for highlighting the role of technical and scientific professionals in laboratory stewardship and providing concrete tools that can be used to develop capacity in these groups. Every level of staff can and should be involved in improving the utilization of clinical laboratory services.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".