OHA Hightlights: Culture, Content, Chemistry and the Odd Loose Cannon
Bibliographic record
Abstract
George Smitherman … a minister with passion and humour Intense listeners everywhere. Culture, Content, Chemistry and the Odd Loose CannonThis year we had a new show in town: Health Achieve2004.Produced by the Ontario Hospital Association (OHA), this was the beginning of a transformation that will make the organization the premium healthcare services show in North America.It is already a benchmark event.Our own informal poll consistently rated this as "The best show ever."Participants were treated to tonic for the mind -new concepts, images, thoughts, plans, impressions, best practices, evidence, principles, opinions, observations and innovations.Those of us who came and participated now have a virtual master's degree in health management.A treasure.It was also the first year that the OHA targeted an audience well beyond its traditional borders.A slick new image, international contributors, global causes and an investment in cross border PR brought attendance to an all-time high.It also ensured more culture, content, comparisons, chemistry and -yes -the odd loose canon.The wide selection of participants offered an unequaled opportunity to meet those we wanted to and those we didn't even know would make a difference.The quality and the range were remarkable.Everyone who came has new ideas to apply, new contacts to enjoy and new energy to employ.Book it for next year.Now.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 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".