A systematic review of online education initiatives to develop students remote caring skills and practices
Bibliographic record
Abstract
The ongoing COVID-19 pandemic has altered caring professions education and the range of technological competencies needed to thrive in today's digital economy. We aimed to identify the various technologies and design strategies being used to help students develop and translate professional caring competencies into remote working environments. Eight databases were systematically searched in February 2021 for relevant studies. Studies reporting on online learning strategies designed to prepare students to operate in emerging digital economies were included. Quality assessment was undertaken using the Effective Public Health Practice Project Quality Assessment Tool and/or the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research. Thirty-eight studies were included and synthesized to report on course details, including technologies being used and design strategies, and study outcomes including curriculum, barriers and facilitators to technology integration, impact on students, and impact on professional practice. Demonstrations of remote care, videoconferencing, online modules, and remote consultation with patients were the most common instructional methods. Audio/video conferencing and online learning systems were the most prevalent technologies used to support student learning. Students reported increased comfort and confidence when working with technology and planning and providing remote care to patients. While a recent influx in research related to online learning and caring technologies was noted, study quality remains variable. More emphasis on assessment, training, and research is required to support students in using digital technologies and developing interpersonal and technological skills required to work in remote settings.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".