Effects of e-learning in a continuing education context on nursing care: a review of systematic qualitative, quantitative and mixed studies reviews (protocol)
Bibliographic record
Abstract
INTRODUCTION: Continuing education (CE) is imperative to the future of professional nursing. The use of e-learning by registered nurses for CE is spreading. A review of systematic reviews will be conducted to develop a broad picture of the effects of e-learning in a CE context on nursing care. METHODS AND ANALYSIS: Systematic qualitative, quantitative and mixed studies reviews published in English, French or Spanish from 1 January 2006 will be included. The outcomes of interest will be extracted and analysed inductively and deductively from the Nursing Care Performance Framework; some themes include nursing resources, nurses' practice environment, processes, professional satisfaction, and nursing sensitive outcomes. Three reviewers will independently screen first the title and abstract of the papers, and then the full texts in order to assess eligibility. Two teams of two reviewers will extract the selected reviews' characteristics and data. The results from various types of reviews will be integrated using a data-based convergent synthesis design. We will conduct a thematic synthesis and transform all quantitative and mixed data into qualitative data. ETHICS AND DISSEMINATION: Ethics approval is not required for review of systematic reviews. We will summarise evidence concerning the negative, neutral and positive effects of various forms of e-learning on different aspects of nursing care. If we find gaps in the literature, we will highlight them and suggest ideas for further research. We will also focus on positive effects and present, if possible, the components and characteristics of e-learning interventions that were found to be successful. We will present this protocol and results in international conferences in nursing, medical, and health informatics domains. We will also submit the results of our work for peer-review publication in a journal indexed in the international bibliographic database of biomedical information.
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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 | 0.171 | 0.226 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".