Planning a graduate programme in public health nutrition for experienced nutrition professionals
Bibliographic record
Abstract
OBJECTIVE: Public health renewal in Canada has highlighted the need for development and expansion of the public health nutrition workforce, particularly in northern and rural communities. The purpose of the present paper is to describe the planning of a more accessible graduate programme for experienced nutrition professionals. The planning effort was challenged by a short timeframe between programme approval and implementation and required intense collaboration with stakeholders and students. DESIGN: The programme planning model developed by The Health Communication Unit (THCU) at the Centre for Health Promotion was used to guide the process. This six-step model was familiar to key stakeholders and involved pre-planning, conducting a situational assessment, establishing goals and objectives, developing strategies and outcome indicators, and monitoring feedback. RESULTS: Resource constraints, short timelines and debates around distance education options presented challenges that were overcome by conducting a thorough needs assessment, creating an advisory committee, engaging key stakeholders in the planning process, and building on existing resources. Extensive involvement of the first cohort of students in ongoing planning and evaluation was particularly helpful in informing the evolution of the programme. CONCLUSIONS: The THCU planning model provided a useful framework for stakeholder collaboration and for planning and implementing the new graduate programme in public health nutrition. Preliminary data suggest that graduates are benefiting from their educational experiences through career enhancement opportunities. The evaluation strategies built into the programme design will be useful in informing ongoing programme development.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".