Public health communication professional development opportunities and alignment with core competencies: an environmental scan and content analysis
Bibliographic record
Abstract
INTRODUCTION: Communication is vital for effective and precise public health practice. The limited formal educational opportunities in health communication render professional development opportunities especially important. Competencies for public health communication describe the integrated knowledge, values, skills and behaviours required for practitioner and organizational performance. Many countries consider communication a core public health competency and use communication competencies in workforce planning and development. METHODS: We conducted an environmental scan and content analysis to determine the availability of public health communication professional development opportunities in Canada and the extent to which they support communication-related core competencies. Three relevant competency frameworks were used to assess the degree to which professional development offerings supported communication competency development. RESULTS: Overall, 45 professional development offerings were included: 16 "formalized offerings" (training opportunities such as courses, webinars, certificate programs) and 29 "materials and tools" (resources such as toolkits, guidebooks). The formalized offerings addressed 25% to 100% of the communication competencies, and the materials and tools addressed 67% to 100%. Addressing misinformation and disinformation, using current technology and communicating with diverse populations are areas in need of improved professional development. CONCLUSION: There is a significant gap in public health communication formalized offerings in Canada and many of the materials and tools are outdated. Public health communication professional development offerings lack coordination and do not provide comprehensive coverage across the communication competencies, limiting their utility to strengthen the public health workforce. More, and more comprehensive, professional development offerings are needed.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".