Preferred modalities for delivering continuing education to the public health workforce: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Continuing education (CE) can help public health professionals maintain and further develop their knowledge and skills to adapt to the changing public health landscape. This scoping review aims to identify the preferred modalities for delivering CE to public health professionals and to determine how equity has been incorporated into public health training. METHODS: Using the PRISMA extension for Scoping Reviews as a guide, we searched four databases for peer-reviewed primary research studies that evaluated public health workforce CE modalities. RESULTS: The review included 33 studies published between 1 January 2000 and 6 August 2019 from over 11 countries. Most articles broadly described their training audience as public health professionals employed by government or non-governmental organizations. Delivery methods included online, in-person or blended learning (combining online and in-person instruction). Learners strongly preferred self-directed approaches. Organizational support, including protected time for professional development during work hours, was an important enabler of training completion. Commonly cited barriers included course duration and a high number of contact hours. CONCLUSION: Findings suggest that there is no single preferred training modality. We identified three elements that influence modality preference: design, delivery and organizational support. Modality should be determined by participants' location, needs and previous experiences to ensure the content is relevant and delivered in a way that equips learners to apply the knowledge gained.
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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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".