Effects of information and communication technology use in nursing and obstetric learning in low- and middle-income countries: A systematic review
Bibliographic record
Abstract
Introduction and objective: Studies on the effects of information and communication technology (ICT) use in nursing and obstetric learning in low- and middle-income countries are limited despite growing scientific evidence that online learning has positive effects. This systematic review aims to identify and synthesize the effects of information and communication technologies utilization in nursing and obstetric learning in low- and middle-income countries.Methods: A search of articles published from 2016 to 2020 on the effects of ICT use in nursing and obstetric learning was conducted in PubMed, CINAHL, Epistemonikos and ERIC.Results: Of 483 articles identified, eleven were reviewed, and eight were found to be relevant. The included articles were synthesized into a narrative synthesis. The effects of using ICT in learning are related to student motivation, autonomy in learning, meaningful acquisition of knowledge and skills. Furthermore, students have a positive perception of the use of ICT in learning.Conclusions: The results of this study on the use of ICTs in nursing and obstetric sciences learning in low- and middle-income countries show that ICTs are used primarily as a medium for distance learning. In addition, it was found that e-learning has several advantages or positive effects. However, many students do not have a personal computer, and they have low or average skills in the use of computer tools, and access to the Internet is low. A limitation of this study is the lack of primary data on the effects of ICT use in obstetric sciences learning in low- and middle-income countries.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".