Public health nutrition practice in Canada: a situational assessment
Bibliographic record
Abstract
OBJECTIVE: Renewed focus on public health has brought about considerable interest in workforce development among public health nutrition professionals in Canada. The present article describes a situational assessment of public health nutrition practice in Canada that will be used to guide future workforce development efforts. METHODS: A situational assessment is a planning approach that considers strengths and opportunities as well as needs and challenges, and emphasizes stakeholder participation. This situational assessment consisted of four components: a systematic review of literature on public health nutrition workforce issues; key informant interviews; a PEEST (political, economic, environmental, social, technological) factor analysis; and a consensus meeting. FINDINGS: Information gathered from these sources identified key nutrition and health concerns of the population; the need to define public health nutrition practice, roles and functions; demand for increased training, education and leadership opportunities; inconsistent qualification requirements across the country; and the desire for a common vision among practitioners. CONCLUSIONS: Findings of the situational assessment were used to create a three-year public health nutrition workforce development strategy. Specific objectives of the strategy are to define public health nutrition practice in Canada, develop competencies, collaborate with other disciplines, and begin to establish a new professional group or leadership structure to promote and enhance public health nutrition practice. The process of conducting the situational assessment not only provided valuable information for planning purposes, but also served as an effective mechanism for engaging stakeholders and building consensus.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".