Modernizing public health communication competencies in Canada: A survey of the Canadian public health workforce
Bibliographic record
Abstract
OBJECTIVES: Since the publication of the Core Competencies for Public Health in Canada in 2008, the public health and communication landscape has changed dramatically. Digital media and infodemics have shifted how practitioners must communicate and respond to health information. The age of the current competency framework, which is relied on for workforce development, alongside emerging public health challenges, have prompted calls for modernized competency statements. This study aims to (i) measure self-reported communication competence in the public health workforce, (ii) measure agreement with new communication competency statements, (iii) identify variation in agreement between sub-groups of professionals, and (iv) explore current and needed communication training. METHODS: Using a mixed-methods online survey, a sample of 378 participants in various Canadian public health roles and regions were asked to rate their current communication competence and agreement with a modernized, evidence-based draft communication competency framework. The survey was distributed in both official languages through partner organizations and social media. Descriptive statistics were performed to assess agreement and variation was analyzed in relation to public health roles and experience. RESULTS: While most participants self-reported communication competence, specific areas were rated lower. All 21 proposed competency statements received high agreement with some variation observed between expertise and experience levels. Demand for communication training is high. CONCLUSION: Strong agreement with statements indicates support for a modernized communication competency framework among sampled professionals. Research to gather more evidence surrounding the communication demands of the public health workforce and observed variation in strong agreement for the proposed statements is underway.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".