Translating caring competencies to remote working environments: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Caring professions attend to the health, educational and social needs of society rather than its material needs. Caring professionals are a vital part of the world's response to COVID-19, yet the global pandemic and its aftermath have significantly changed the ways in which care is provided. The rapid pivot to remote care, where the essential caring cues and opportunities are not as readily available, has put unprecedented pressure on caring professions. There is currently a lack of clear understanding and accepted standards for teaching caring profession students how to provide care remotely. The objective of this systematic review is to identify and assess the ways in which educators can integrate online learning opportunities to help students develop effective caring practices and translate these into today's remote and virtual care environments. METHODS AND ANALYSIS: This systematic review will consider diverse quantitative, qualitative and mixed-methods studies of innovative online education initiatives and required technology for caring profession education. Articles will be retrieved from academic databases and limited to articles reporting primary data and published in English within the last 10 years. Data extraction procedures will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. The methodological quality of all studies will be assessed using the Effective Public Health Practice Project Quality Assessment Tool and/or the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research. Study characteristics will be tabulated and narratively synthesised to integrate and explore relationships within the data. ETHICS AND DISSEMINATION: No ethics approval is required to conduct this review. Review findings will be disseminated through peer-reviewed publications, conference presentations and be used to inform and guide caring profession education policy, practice and research agendas with the goal of improving education for caring profession students, and care for the patients, clients and learners they serve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.126 | 0.110 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.020 | 0.012 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.077 | 0.017 |
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".