The Core Competencies for Public Health in Canada: Opportunities and Recommendations for Modernization
Bibliographic record
Abstract
CONTEXT: The 2008 Public Health Agency of Canada's (PHAC's) "Core Competencies for Public Health in Canada" (the "Canadian core competencies") outline the skills, attitudes, and knowledge essential for the practice of public health. The core competencies represent an important part of public health practice, workforce development, and education in Canada and internationally. However, the core competencies are considered outdated and are facing calls for review, expansion, and revision. OBJECTIVE: To examine the literature on public health competencies to identify opportunities and recommendations for consideration when reviewing and updating the Canadian core competencies. METHODS: This narrative literature review included 4 components: 3 literature searches conducted between 2021 and 2022 using similar search strategies, as well as an analysis of competency frameworks from comparable jurisdictions. The 3 searches were conducted in collaboration with the Health Library to identify core competency-relevant scholarly and gray literature published in English since 2007. Reference lists of sources identified were also reviewed. During the data extraction process, one researcher screened each source, extracted competency-relevant information, and categorized these data into key findings. RESULTS: After identifying 2392 scholarly and gray literature sources, 166 competency-relevant sources were included in the review. Findings from these sources were synthesized into 3 main areas: (1) competency framework methodology and structure; (2) competencies to add; and (3) competencies to modify. DISCUSSION: These findings demonstrate that updates to Canada's core competencies are needed and overdue. Recommendations to support this process include establishing a formal governance structure for the competencies' regular review, revision, and implementation, as well as ensuring that priority topics applicable across all competency categories are integrated as overarching themes. Limitations of the evidence include the potential lack of applicability and generalizability to the Canadian context, as well as biases associated with the narrative literature review methodology.
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 | 0.058 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".