Assessment of Preparedness for Remote Teaching and Learning to Transform Health Professions Education in Sub-Saharan Africa in Response to the COVID-19 Pandemic: Protocol for a Mixed Methods Study With a Case Study Approach
Bibliographic record
Abstract
BACKGROUND: The current COVID-19 pandemic is affecting all aspects of society worldwide. To combat the pandemic, measures such as face mask-wearing, hand-washing and -sanitizing, movement restrictions, and social distancing have been introduced. These measures have significantly disrupted education, particularly health professions education, which depends on student-patient contact for the development of clinical competence. The wide-ranging consequences of the pandemic are immense, and health professions education institutions in sub-Saharan Africa have not been spared. OBJECTIVE: This paper describes a protocol for assessing the preparedness of selected health professions education institutions in sub-Saharan Africa for remote teaching and learning during the COVID-19 pandemic. METHODS: A mixed-methods design with a case study approach will be used. The awareness, desire, knowledge, ability, and reinforcement model of change was selected as the conceptual framework to guide the study. Eight higher education institutions in 6 sub-Saharan countries have participated in this study. Data will be collected through electronic surveys from among whole populations of academic staff, students, and administrators in undergraduate medicine and nursing programs. Qualitative and quantitative data from each institution will be analyzed as a case study, which will yield an inventory of similar cases grouped for comparison. Quantitative data will be analyzed for each institution and then compared to determine associations among variables and differences among programs, institutions, or countries. RESULTS: Our findings will provide information to higher education institutions, particularly those offering health professions education programs, in Africa regarding the preparedness for remote teaching and learning to influence efforts related to web-based teaching and learning, which is envisaged to become the new normal in the future. CONCLUSIONS: This study has not received any funding, and any costs involved were borne by individual consortium members at the various institutions. Ethics approval from the institutional review board was obtained at various times across the participating sites, which were free to commence data collection as soon as approval was obtained. Data collection was scheduled to begin on October 1, 2020, and end on February 28, 2021. As of this submission, data collection has been completed, and a total of 1099 participants have been enrolled. Data analysis has not yet commenced. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28905.
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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 | 0.075 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".