Engaging Laboratory Staff in Stewardship: Barriers Experienced by Medical Laboratory Technologists in Canada
Bibliographic record
Abstract
BACKGROUND: Laboratory stewardship programs aim to improve the use of laboratory resources, including reducing inappropriate testing. These programs should engage all healthcare stakeholder groups, including all levels of laboratory staff. Medical laboratory technologists (MLTs) are highly skilled professionals and are well positioned to play a supportive role in stewardship but may be overlooked. The aim of this study is to identify the barriers to MLT participation in stewardship activities. METHODS: We developed and disseminated a self-administered survey to MLTs in Canada to assess their knowledge and attitudes toward inappropriate laboratory utilizatioz and explore perceived barriers to taking on an active role in stewardship initiatives. Themes were identified in open-ended responses and mapped to the Theoretical Domains Framework (TDF). RESULTS: MLTs feel accountable for helping ensure appropriate resource use and recognize that it is an important issue to address. However, they experience significant barriers and have low intention to act. The self-reported barrier most frequently described was lack of time arising from excessive workloads, but other constraints exist. Themes mapped to the TDF most strongly in the domain of environmental context and resources, supporting evidence that workplace structure and culture play key roles in impacting this group. CONCLUSIONS: To meaningfully engage MLTs in stewardship activities, these barriers should be addressed. Highlighting MLT expertise and creating communication structures and opportunities for their unique contributions may be fruitful.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".