Information literacy skills and training of licensed practical nurses in Alberta, Canada: results of a survey
Bibliographic record
Abstract
BACKGROUND: Although information literacy skills are recognized as important to the curriculum and professional outcomes of two-year nursing programs, there is a lack of research on the information literacy skills and support needed by graduates. OBJECTIVE: To identify the information literacy skills and consequent training and support required of Licensed Practical Nurses (LPNs) in Alberta, Canada. METHOD: An online survey using a random sample of new graduates (graduated within 5 years) from the registration database of the College of Practical Nurses of Alberta (CLPNA). RESULTS: There was a 43% response rate. Approximately 25-38% of LPNs felt they were only moderately or to a small extent prepared to use evidence effectively in their professional practice. LPNs use the internet and websites most frequently, in contrast to library resources that are used least frequently. Developing lifelong learning skills, using information collaboratively, and locating and retrieving information are areas where LPNs desire more effective or increased training. CONCLUSION: The results suggest there are significant gaps in the preparedness and ability of LPNs to access and apply research evidence effectively in the workplace. There are several areas in which the training provided by Librarians appears either misaligned or ineffective.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".