“Public health without effective health communication is really nothing”: Key informant interviews and thematic analysis as part of a multi-step research process to develop modernized public health communication competencies
Bibliographic record
Abstract
Background: Communication is central to effective public health practice, with all roles and functions requiring proficiency in communication. A competency-based approach to public health enhances workforce development and helps ensure adaptability and collaboration by equipping professionals with the practical skills needed to address complex and evolving public health challenges, including through communication. Methods: Semi-structured interviews and reflexive thematic analysis were conducted to explore the perspectives of public health communication experts (researchers and practitioners) regarding the importance of public health communication, the challenges and opportunities faced by the field of practice, and the specialized competencies required for strengthened communication in modernized public health practice. Results: Twelve key informants were interviewed, and seven interrelated themes were generated from the data. The themes were organized in three main areas: the importance of public health communication, the various levels of influence on effective communication, and the support needed for strengthened communication capacity. Participants stressed the importance of partnerships and collaboration, dedicated resources, ongoing professional development, and tools to facilitate the implementation of the specialized competencies. Conclusions: A competency-based approach, including specialized roles and education and training programs aligned with updated communication competencies, will empower public health to tackle modern challenges. This research contributes to a multi-step research project to develop a Canadian public health communication competency framework and supports ongoing efforts to strengthen the public health workforce in Canada.
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 | 0.054 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".