Novice Nurse Information Needs in Paper and Hybrid Electronic-Paper Environments: A Qualitative Analysis
Bibliographic record
Abstract
A within group, laboratory, experimental study of nurse information seeking was conducted. As a part of the study, 35 novice nurses assessed and planned the care of two patients in two simulation environments: a paper (PR) environment and a hybrid (HY) environment [i.e., part of the environment was made available in electronic form via an electronic patient record (EPR) and part of it was paper-based]. Subjects were asked to "think aloud" in each environment and participated in a cued recall session following participation in the simulations. Subjects' verbalizations and actions were audio and video recorded and then transcribed. In the first phase of the study audio and video data were qualitatively coded using Model Based Coding with concepts from Newcomer Information Seeking Theory (NIST). This paper presents the qualitative results of this study with a focus upon the types of information used by novice nurses during the assessment and planning of patient care. Qualitative findings revealed novice nurses used referent, relational and appraisal information (as predicted by NIST theory and research) including information composed of more than one type of information (e.g., referent-relational). Two new types of information emerged from the qualitative data - situational task and situational organization information.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".