User satisfaction with information and communication technologies in nursing and midwifery schools in sub-Saharan Africa: A systematic review
Bibliographic record
Abstract
Background and objective: Information and communication technologies are often used in universities in sub-Saharan Africa to train nurses and midwives. However, user satisfaction with information and communication technologies in nursing and midwifery schools in sub-Saharan Africa has not been well documented. The objective of this study is to synthesize user satisfaction with information and communication technologies in nursing and midwifery schools in sub-Saharan Africa.Methods: A systematic review was conducted. Three electronic databases (PubMed, CINAHL, and ERIC) were consulted. Two reviewers independently conducted the selection of eligible publications based on the inclusion and exclusion criteria. Data were extracted and quality assessed by four team members. Qualitative, quantitative, or mixed studies conducted in sub-Saharan African countries published from 2018 to 2021 were included.Results: The majority of students used smart mobile phones. Access to the internet connection was via their mobile phone or tablet. In terms of their ability to use mobile devices, the majority of students were good users. The rest were divided between experts in mobile use and limited users. The majority of teachers were open to the use of word processing, PowerPoint presentations, and blended learning. Other reasons for satisfaction include the use of information and communication technologies during clinical placements, online assessments, the creation of discussion forums, and live discussions with colleagues.Conclusions: Students are satisfied with the use of information and communication technologies. The administration must equip itself with the means to use them in teaching by motivating and supporting teachers. To do this, it must take into account the results of regular assessments to provide a better learning environment. Using information and communication technologies could become a quality criterion for a high-performance university.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".