The Public Health Communication Competency Framework: Results from a multi-method and consensus-building process
Bibliographic record
Abstract
OBJECTIVE: Public health practice necessitates effective health communication. Now, more than ever, public health practitioners must possess the competencies - the skills, knowledge, and attitudes - to communicate effectively. Using a robust, multi-step, multi-method research process, we sought to develop a set of communication competency statements for public health professionals in Canada, in communication-focused roles. METHODS: Following earlier research steps that included scoping reviews, environmental scans, a national survey (n = 378), and key informant interviews (n = 12), which established an initial set of competency statements, a modified Delphi technique was conducted with a panel of public health communication experts which included two online survey rounds (n = 19, n = 18) with one virtual meeting (n = 7) in between to develop consensus on the initial competency statements. An a priori threshold for consensus of 75% agreement was set. RESULTS: After the second Delphi survey, participants reached consensus on all competency statements with 96% of participants agreeing with each statement on average, an increase from the first Delphi survey. Between the two Delphi surveys, revisions were made to the statements based on quantitative and qualitative participant feedback. CONCLUSION: The research has resulted in 18 communication competency statements comprising the Public Health Communication Competency Framework, which are intended to serve as a roadmap for public health professionals whose roles encompass communication, guide curricula development in public health programs, catalyze professional development in communication, and support public health systems to achieve the essential public health functions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.303 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".