Achieving shared values: A mixed methods study and multi-method model of how to effectively educate nurses about e-professionalism
Bibliographic record
Abstract
Background and objective: Professional socialisation is the method by which nurses become ‘professionals’, demonstrating the values and behaviours accepted by the profession in both the online (e-professionalism) and offline environment. Understanding the concept of e-professionalism and the values associated with online behaviours is an important component of professional practice. This mixed methods project explored nurse’s perspectives about what is acceptable to do on social media and used an evidence-based decision-making tool (A2A) to assess perspectives about whether behaviours in social media are professional or unprofessional is reliable and valid for use in nurse education.Methods: Quantitative data was gathered for a series of five vignettes nurses were required to use the A2A tool to score each on the basis of professionalism. To assess the reliability and validity of the tool, participants were asked to complete this task on two occasions n = 122 nurses completed the initial survey and n = 48 repeated the survey. Following this, qualitative data were gathered via focus groups to explore the reasons why consensus could not be achieved.Results: Findings show that, even with a structured tool there are still variations in what is and is not deemed to be professional behaviour. There was limited reliability and validity for individual use of the tool, but clinical staff found it useful and relevant to practice. Focus groups (three, n = 8) then explored the concept of e-professionalism further to establish the reasons why consensus is not achieved despite the presence of a structured tool. Two main themes were found 1) the role of values in achieving consensus and 2) the role of tools in achieving consensus and in nurse education. The complex interdependence of personal-social-professional values (competing or complementary) were found to be a significant reason why consensus about acceptable online behaviours was not achieved by using the structured tool.Conclusions: A multi-method model to approach nurse education is proposed. It uses a combination of tools and approaches to explore the personal-social-professional domains, navigate competing values and teach practical skills for effective use of social media platforms.
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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 | 0.135 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".