e-Delphi Technique in Postgraduate Registered Nursing Education and Competency Development: A Scoping Review
Bibliographic record
Abstract
Background: It is common for nursing programs in Canada to hire part-time clinical instructors (CIs) to teach the practical components of curricula. Although experts in their field, these Registered Nurses (RNs) don’t have any formal preparation in education. Additionally, there are no current established competencies for CIs in Canada. This void in the Canadian literature warrants a search of competency development using the e-Delphi technique. The e-Delphi technique is a commonly used surveying technique for competency development in healthcare. Objective: To understand the extent, range, and nature of evidence of the use of the e-Delphi technique and critically appraise its use in postgraduate nursing education and nursing competency development to establish the feasibility of application to clinical nurse educator competency (CNEC) development in Canada. Methods: Two independent researchers conducted a scoping review to determine the extent, range, and nature of evidence of the use of the e-Delphi technique and critically appraise its use in postgraduate nursing education and nursing competency development. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute Reviewers’ Manual were used to guide the review. Results: The main components of e-Delphi that were extracted from the literature for review included the purposes of the studies, background of expert panel members, methods of e-Delphi used, reported level of consensus, number of rounds to meet consensus, time between rounds of questions, number of participants and changing participants. The purposes of the reviewed studies demonstrated that e-Delphi is a preferred method for developing or revising competencies for post baccalaureate programs. However, the other key components that were extracted revealed much variation in the use of e-Delphi by researchers. Most importantly, researchers need to ensure they report on the backgrounds of the expert panel members, decrease the time between the survey rounds and avoid introducing new participants in later rounds of the surveys to ensure consistency and methodological rigour of e-Delphi. Conclusion: There are variations in how e-Delphi is used in the literature for competency development, but if the authors are transparent with all phases of the method used, it is evident that it can significantly contribute to the advancement of future CNEC development in Canada.
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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 | 0.356 | 0.427 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.045 | 0.042 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".