Developing a program of research for an applied public health chair in public health education and population intervention research.
Bibliographic record
Abstract
In 2008 the Canadian Institutes of Health Research (CIHR) Institute of Population and Public Health (IPPH), in partnership with the Public Health Agency of Canada and the Centre de Recherche en Prevention de l’Obesite, announced the funding of 15 Applied Public Health Research Chairs across Canada. This initiative has five objectives: to support nationally relevant and innovative public/population health intervention research and knowledge translation; to foster strong linkages between the research chairs and the public health system; to support the development of graduate public health programs; and to educate and mentor current and future public health researchers, practitioners, and policy-makers. Among the Chairs, many disciplines are represented. I was fortunate enough to be one of two nurses in Canada to receive this award. Because public health (PH) work is inherently interdisciplinary, the work of the Chairs is also interdisciplinary; however, PH nurses represent the largest segment of the PH workforce, so it is critical that a nursing perspective be brought to CIHR’s collective capacity-building effort in PH. I feel privileged to be able to contribute to this through my mentoring of nursing graduate students and by participating in the development of a graduate diploma in Public Health Nursing. This will be a stream in the Master of Public Health program at the University of Victoria’s new School of Public Health and Social Policy, which will function in close collaboration with the School of Nursing.
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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 | 0.105 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".