Nursing faculty and student experiences with information literacy: A pilot study
Bibliographic record
Abstract
Background : A nursing workforce equipped with information literacy skills is vital to patient care delivery based on evidence. Nursing faculty must educate and prepare nursing students at all levels with these skills. Information literacy skills development is nursing programs is often addressed irregularly and in many cases libraries are charged with teaching these skills. This manuscript described the information literacy experiences of nursing students and faculty at a teaching university in the Southeastern United States. Research questions include 1) What do nursing faculty and nursing students know about using online information sources? 2) Do nursing faculty and nursing students use reliable online information sources on a regular basis? and 3) What types of technology do nursing faculty and students encounter and use in the classroom and clinical setting? Methods: A descriptive study was conducted to determine nursing faculty and nursing students’ experiences involving information literacy in the classroom and clinical setting. All nursing students and faculty were invited to participate and were provided with details about the study purpose and survey via email. Participation was strictly anonymous and voluntary. Data collection was conducted using an online survey that consisted of 20 multiple choice questions, 6 of which contained follow-up questions. Results: Sixty-six participants responded to the online survey. Fifteen (22.8%) were faculty and fifty-one (77.3%) were students. Data analysis reveals that most participants rate information literacy abilities that correlated with patient care as very valuable (59%, n = 39) or valuable (41%, n = 26). Additional results including participants’ responses to questions regarding information informing nursing practice, personal computer and information literacy skills, and barriers to accessing evidence-based information in the clinical setting were reported. Conclusions: Healthcare providers recognize the need for information literacy skills in the clinical setting but often lack the skills associated with locating, accessing, evaluating, and applying information needed for evidence-based practice. By understanding nursing faculty and students’ experiences with information literacy, learning activities can be designed and implemented that meet the needs of the study population.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".