<p>Use of guest speakers in nursing education: an integrative review of multidisciplinary literature</p>
Bibliographic record
Abstract
Background: The purpose of this paper is to review existing literature regarding the use of guest speakers in multidisciplinary education and discuss the implication of the findings in nursing education. Method: An integrative review including 18 papers from 13 disciplines. Results: The evidence indicates that guest speakers can be invited by a variety of stakeholders with various motivations. Individuals from both discipline-related practical fields and academic institutions are frequently invited. Guest speakers have the ability to promote better teaching outcomes. Having a database of potential speakers decreases the work of selecting future guest speakers. Guest speakers can also be virtual with the use of guest speakers growing. A flow model of using guest speakers, including seven steps, has been articulated. Involving guest speakers promotes reciprocal benefits, where guest speakers, students, and professors have dual roles and contribute to and gain from each other. Conclusion: Findings from multiple disciplines regarding guest speakers in higher education can inform the best possible practices and procedures in how to get the best use of guest speakers in undergraduate nursing education. However, further research and study are warranted within the discipline of nursing to produce findings directly applicable to nursing education. Keywords: guest speaker, guest lecture, online, multi-disciplinary literature, review
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".