Struggling to Improve Our Understanding of Nursing Student Information Needs
Bibliographic record
Abstract
A review of: Gannon-Leary, Pat, Graham Walton, Raffik Cader, Julie Derbyshire, and Ann Smith. “Use of Evidence by Nursing Students: An Interdisciplinary Study.” Library & Information Science Research 28.2 (Mar. 2006): 249-64. Abstract Objective – To identify the sources used by student nurses when undertaking a health needs analysis of particular client groups, and to determine students’ ease in accessing source materials. Design – Case series, incorporating citation analysis and focus groups. Setting – Honours Nursing Studies program (Adult Branch), Northumbria University, Newcastle upon Tyne, United Kingdom. Subjects – Citation analysis: 40 students undertaking health needs analysis assignment. Focus groups: 2 groups of 8 students. Methods – Subjects for the citation analysis were not randomly selected. Citation analysis was performed and references tabulated using a previously piloted pro-forma. Distribution of references between sources was measured using quantitative analysis. Focus groups consisted of self-selecting volunteers from the student cohort undertaking the assignment. Issues of potential bias/coercion relating to research team members being group participant tutors were addressed. Results were analysed through the categorisation of key themes. Main results – The mean number of sources cited per assignment was 16. More than half (56%) of the sources were published after 1999. Twenty-nine percent of citations were published between 1996 and 1999, and 12% between 1990 and 1995. Only small percentages were published in the 1980s (2%) and 1970s (1%). Journals (37%) formed the largest body of references, followed by books at 27%. UK government publications accounted for 14% of total references; Web-based resources totalled 12%. Students acknowledged the importance of supporting assignments with references. Government publications were recognised as high-quality pieces of evidence. Difficulty was experienced extrapolating evidence from different care settings and moving outside of usual parameters. Students indicated that statistical data was hard to find. Those without home Internet facilities had greater difficulty in accessing information than students with such facilities. Support from information professionals was received, but quality was variable. Conclusion – Doubts exist as to whether opportunities for student learning to inform educators are being fully exploited. Upon completion of assignments students are arguably in a better position to debate subjects more deeply and so advance understanding. Potential for peer group development may be lost. Collaboration between nursing academics and information professionals offers potential for nursing students and health professionals to advance their exploitation and evaluation of information resources.
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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 | 0.037 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.036 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".