Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Understanding the functionality, benefits, and limitations of generative artificial intelligence (GAI) is important for nurses and nursing students. PURPOSE: This study explored nursing students' perspectives on GAI after a guided learning activity in which students used a chatbot to answer a clinical question. METHODS: A qualitative approach using reflective thematic analysis of written reflections was conducted with 19 nursing students in a nursing baccalaureate completion program. RESULTS: Student reflections demonstrated 4 themes: surprisingly familiar; the importance of critical thinking and external validation; a good summary lacking depth and nuance; and cautious optimism. Two subthemes were also identified: validation is time-consuming and a new perspective. CONCLUSIONS: Learning activities using GAI influence students' knowledge and attitudes and instill critical awareness of the advantages and limitations of this technology. Additional emphasis on bias in GAI is needed when teaching about AI.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it