Developing education for e-professionalism-Mixed methods evaluation of the impact of an evidence based educational tool for nurses
Bibliographic record
Abstract
Background and objective: Research literature has long suggested a need for educational tools that raise awareness of e-professionalism, promote reflective practice and skills to manage what is shared publicly in social media. This study aimed to evaluate the utility of an evidence-based educational tool (Awareness to Action, A2A) on the topic of e-professionalism, designed specifically to raise [personal and professional] awareness about the risks associated with social media platforms and the information that is shared within them.Methods: Realist action research, collecting quantitative and qualitative data via the A2A quiz and focus groups. Results: The A2A quiz was taken by n = 17 participants and n = 8 participants took part in the focus groups. Data showed that the tool was deemed as ‘really’ relevant to practice. Three main themes were found in the data 1) Defining and understanding e-professionalism, 2) The wider context of social media and e-professionalism and 3) The impact of the A2A tool.Discussion and conclusions: Nurses and nursing students are aware of e-professionalism but less able to define it clearly, favouring practical examples of what they consider to be acceptable. The blurring of social-personal-professional boundaries is a challenge when using social media, as is the general nature of social media but the tool was deemed as helpful in navigating these challenges. Educational tools, such as the A2A tool can have a positive impact on nurses, students and - as it is free to access and easy to complete - potentially other healthcare professionals’ behaviours online, fostering reflection and positively changing behaviours/perspectives.
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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 | 0.243 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".