Knowledge exchange: the role of multidisciplinary Schools of Public Health in promoting health \nand wellbeing
Bibliographic record
Abstract
This one hour knowledge exchange forum aims to provide participants with an opportunity to explore and learn from the way in which multidisciplinary Schools of Public Health play a role in promoting health and wellbeing, in three contrasting policy contexts: Scotland, Canada and England. \nParticipants will receive high level findings from a brief literature review of this area of work. \nThe three contrasting situations will each be described by a lead informant using the following \nheadings: \n Vision \n Drivers and change \n Experiences and relationships \n Logistics and practicalities \n Lessons learned so far. \nA key discussant will then offer some initial feedback on the presentation, identifying key questions, and issues for discussion. \nA plenary discussion follows to take a view on what has been learnt overall. The session will be chaired by Professor Peter Kelly, Acting Regional Director of Public Health. \nThe 3 lead informants will be: \nAllan Best, Managing Director, InSource, Clinical Professor, School of Population and Public Health, University of British Columbia Sally Haw, Senior Scientific Adviser, Scottish Collaborative for Public Health Research and Policy \nAlyson Learmonth, Head of School of Public Health, North East of England. \nDiscussants are locally experienced in the field of public health from diverse backgrounds: Peter Wright, Environmental Health & Trading Standards Manager; Alison Steven, Senior Lecturer in Knowledge Translation in Public Health, and John Woodhouse, Consultant in Public Health, Clarity & Partnership Limited.
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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 | 0.069 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.004 | 0.050 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".