Playing in the Sandbox: Considerations When Leading or Participating on a Multidisciplinary Research Team
Bibliographic record
Abstract
Research involves working to find answers to questions. Research questions of interest to pharmacists generally relate to any aspect of the discovery, effect, or use of medications, as well as the role of pharmacists within the health care system. This article describes the key aspects of leading or participating on a multidisciplinary research team so as to provide guidance to pharmacists involved in the research enterprise. The research questions that emerge from topics well suited to the expertise and experience of a pharmacist are often, by their inherent nature, best addressed by a team of people from different scientific backgrounds: individuals who can form a multidisci plinary team that will work together to develop and carry out all aspects of the research project. Although not all research questions require multidisciplinary research teams, it is beneficial to work as a team, with different perspectives, knowledge, and skills available to answer complex, multifaceted research questions. The team can integrate ideas across disciplines, advance thinking within and across disciplines, and go deeper and broader to create knowledge that can be used to develop novel, more meaningful solutions. Individual team members can also get to know new people (their fellow team members), achieve greater personal satisfaction, and have more fun along the way. Working as a team helps to improve knowledge translation of findings in multiple sectors, thereby increasing uptake and sustainability, and also creates wider networks and encourages development of professional relationships. Research funding agencies worldwide, including the
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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 | 0.304 | 0.361 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.054 | 0.022 |
| Scholarly communication | 0.043 | 0.029 |
| Open science | 0.011 | 0.035 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.030 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".