Supporting Multidisciplinary Analytic Skills: An Innovative Training Platform for Capacity Building
Bibliographic record
Abstract
ABSTRACTObjectivesTo serve the emerging multidisciplinary skills and capacity building needs of Population Health Researchers within a rapidly diversifying field. Population Health Research is inherently interdisciplinary, multifaceted and firmly rooted in the evolving connections between place, time and related socioeconomic processes. To excel in this rapidly diversifying field, individuals require a broad range of multidisciplinary skills. Supporting the development of these skills through innovative training platforms is one key way to build capacity for emerging 21 Century researchers and health professionals. ApproachEstablishment of an innovative research training platform that supports skill development in a timely, collaborative and practiced based environment. The growing importance of data analytics and spatial thinking as it pertains to the worlds growing health concerns, be they social, physical or environmental – demands approaches that serve real time and remotely accessed, exploratory and highly collaborative research environments. A case example will be provided concerning a tri-party training platform that is serving the multidisciplinary skill requirements of new and mid-career population health professionals. Designed in collaboration with a tri-university research platform, the innovative, practice-based training environment both mirrors and supports many of the day to day skill development needs of health and social science researchers. ResultsThe multidisciplinary focus of this specialized training platform is successfully addressing the skill development needs of a diverse cross section of health research professionals. Trainees are bringing a wealth of experience and knowledge to share with their online colleagues, supporting a rich, practice based education and skills development environment. Those enrolled in the program possess backgrounds ranging from Population and Public Health, Epidemiology, Statistics and Sociology to Medicine to Psychology, Geography, Biostatistics and International Health. ConclusionProviding timely, practical, hands-on analytic skills training is critical to building the capacity of new and mid-career researchers and health professionals. Direct application of these new skills is an essential outcome and best measure of success. We are listening to our trainees and learning as we grow.Read what trainees are saying about our certificate courses.https://www.popdata.bc.ca/etu/testimonials/PHDA
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".