Assessing Communication Competencies in Canadian MPH Program Curriculum: A Content Analysis of Communication Courses
Bibliographic record
Abstract
OBJECTIVES: Communication plays a pivotal role in addressing modern and complex public health challenges. Our study assessed the extent to which communication-related course outlines in Canadian master of public health (MPH) programs aligned with national and international public health competency frameworks in their coverage of communication competencies. METHODS: We conducted an environmental scan and content analysis of MPH courses relevant to public health communication in 2022 and 2023. We used university and program websites and Google to conduct initial searches and obtain course outlines, supplementing these searches with a survey. We developed a codebook based on public health competencies and pedagogical best practices, capturing variables for communication competencies, audiences, channels, tools, and techniques. Two researchers independently coded course outlines. Descriptive statistics evaluated how these courses address communication-related public health competencies. RESULTS: We obtained 11 course outlines offered from 2010 through 2023. The focus of the included courses varied, with health communication (n = 3), knowledge mobilization (n = 3), and risk and/or crisis communication (n = 2) being the most common. All courses broadly aligned with communication competencies related to communication with different audiences (n = 11), and mobilizing (n = 9), interpreting (n = 11), tailoring (n = 9), and facilitating (n = 9) communication. Using technology (n = 4) and media (n = 6), addressing mis/disinformation (n = 1), and communicating with diverse audiences (n = 3) had less alignment. CONCLUSIONS: Findings revealed gaps in the coverage of key competencies, particularly in addressing mis/disinformation, leveraging technology and media, communicating with diverse populations, health literacy, and crisis communication. Ongoing review of curriculum would ensure alignment with evolving competencies and public health demands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".