Evaluation of a continuing professional development strategy on COVID-19 for 10 000 health workers in Ghana: a two-pronged approach
Bibliographic record
Abstract
BACKGROUND: COVID-19 has created unprecedented challenges for health systems worldwide. Since the confirmation of the first COVID-19 case in Ghana in March 2020 Ghanian health workers have reported fear, stress, and low perceived preparedness to respond to COVID-19, with those who had not received adequate training at highest risk. Accordingly, the Paediatric Nursing Education Partnership COVID-19 Response project designed, implemented, and evaluated four open-access continuing professional development courses related to the pandemic, delivered through a two-pronged approach: e-learning and in-person. METHODS: This manuscript presents an evaluation of the project's implementation and outcomes using data for a subset of Ghanaian health workers (n = 9966) who have taken the courses. Two questions were answered: first, the extent to which the design and implementation of this two-pronged strategy was successful and, second, outcomes associated with strengthening the capacity of health workers to respond to COVID-19. The methodology involved quantitative and qualitative survey data analysis and ongoing stakeholder consultation to interpret the results. RESULTS: Judged against the success criteria (reach, relevance, and efficiency) the implementation of the strategy was successful. The e-learning component reached 9250 health workers in 6 months. The in-person component took considerably more resources than e-learning but provided hands-on learning to 716 health workers who were more likely to experience barriers to accessing e-learning due to challenges around internet connectivity, or institutional capacity to offer training. After taking the courses, health workers' capacities (addressing misinformation, supporting individuals experiencing effects of the virus, recommending the vaccine, course-specific knowledge, and comfort with e-learning) improved. The effect size, however, varied depending on the course and the variable measured. Overall, participants were satisfied with the courses and found them relevant to their well-being and profession. An area for improvement was refining the content-to-delivery time ratio of the in-person course. Unstable internet connectivity and the high upfront cost of data to access and complete the course online were identified as barriers to e-learning. CONCLUSIONS: A two-pronged delivery approach leveraged distinct strengths of respective e-learning and in-person strategies to contribute to a successful continuing professional development initiative in the context of COVID-19.
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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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".