International research collaborations: Lessons learned and practical tips
Bibliographic record
Abstract
The importance of creating international research collaborations for the purpose of producing high quality educational research aimed at enhancing or promoting consistency in health care practice has become even more apparent since the start of the COVID-19 pandemic. These cross-country, and sometimes cross discipline collaborations can be particularly beneficial when conducting research into aspects of clinical practice identified internationally as primary goals for patient safety improvement. An example is the commonly shared goal to improve early detection of clinical deterioration by health professionals since this continues to be reported internationally as a significant patient safety risk and suboptimal aspect of care delivery (Haddeland et al., 2018, Lee and Quinn, 2019, Goldsworthy et al., 2022, Goldsworthy et al., 2022). However, creating an effective international collaboration can be a complex process. This paper aims to describe the process of creating a successful collaboration between research teams at universities in Canada, Australia, England, and Scotland, prior to and during a global pandemic. A description of how the team was built and sustained will be described along with the benefits, challenges, and results of this collaboration, to provide strategies on how this can be effectively achieved.
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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 | 0.119 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".