On the same page? A qualitative study protocol on collaboration in a multi-laboratory preclinical study
Bibliographic record
Abstract
INTRODUCTION: Medical advancements are slow to reach the patient bedside due to issues with knowledge translation from preclinical studies. Multi-laboratory preclinical studies are a promising strategy for addressing the methodological deficiencies that weaken the translational impact of single laboratory findings. However, multi-laboratory preclinical studies are rare and difficult, requiring strong collaboration to plan and execute a shared protocol. In multiteam systems such as these, collaboration is enhanced when members have cohesive ways of thinking about their goals and how to achieve them-that is, when they have "shared mental models". In this research project, we will examine how members of Canada's first multi-laboratory preclinical study build shared mental models and collaborate in the execution of their study. METHODS: Six independent labs in Canada will conduct a preclinical study using a common protocol. To investigate mental models and collaboration in this multiteam system we will conduct a longitudinal qualitative study involving interviews at four time points, team observation, and document analysis. We will analyze interview transcripts using deductive coding to produce a matrix analysis of mental model content over time and inductive coding to produce a thematic analysis of members' experiences of collaboration over time. We will also triangulate data sources to "tell the story" of teamwork, capturing events and contextual information that explain changes in mental models and collaboration over time. DISCUSSION: This study will be one of the most comprehensive longitudinal analyses of a real-world multiteam system, and the first within a preclinical laboratory setting. The results will contribute to our understanding of collaboration in multiteam systems, an organizational form increasingly used to tackle complex scientific and social problems. The results will also inform the implementation of future multi-laboratory preclinical studies, enhancing the likelihood of effective collaboration and improved 'bench to bedside' translation.
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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 | 0.093 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".