Utilizing grounded theory to explore the information-seeking behavior of senior nursing students
Bibliographic record
Abstract
BACKGROUND: The ability to find and retrieve information efficiently is an important skill for undergraduate nursing students. Yet a number of studies reveal that nursing students are not confident in their library searching skills and encounter barriers to retrieving relevant information for assignments. OBJECTIVES: This grounded theory study examined strategies used by students to locate information for class assignments and identified barriers to their success. METHODS: Purposive sampling was used to recruit eleven students, who were asked to record their searching processes while completing a class assignment, and semi-structured, open-ended, audiotaped interviews took place to discuss the students' journals and solicit additional data. Methods of information seeking, strategies used to find information, and barriers to searching were identified. RESULTS: Students' main concern was frustration caused by the challenge of choosing appropriate words or phrases to query databases. The central theme that united all categories and explained most of the variation among the data was "discovering vocabulary." CONCLUSIONS: Teaching strategies to identify possible words and phrases to use when querying information sources should be emphasized more in the information literacy training of undergraduate nursing students.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".