Evaluating the Impact of Hybrid Electronic-paper Environments Upon Novice Nurse Information Seeking
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate the effects of hybrid environments (i.e. where part of the patient record is paper-based and part of it is electronic) upon aspects of novice nurse information seeking (i.e. amount of information accessed, choice of key information sources, type of information and use of information seeking tactics). METHODS: A within group, laboratory, experimental study was conducted using two simulated environments (i.e. a paper environment and a hybrid environment). Thirty-five novice nurses participated in the study. RESULTS: Findings revealed significant differences between the paper and hybrid environments in terms of their effects upon aspects of novice nurse information seeking. Subjects accessed: 1) less information in the hybrid environment than the paper environment, 2) more non-electronic sources of information were accessed by novice nurses in the hybrid environment, and 3) novice nurses used more passive information seeking tactics in the hybrid environment than the paper environment. Qualitative findings from the cued recall data revealed subjects experienced increased cognitive load in the hybrid environment. CONCLUSIONS: Hybrid environments may affect aspects of novice nurse information seeking. Future research should explore the effects of hybrid environments upon the information seeking of other types of health professionals (e.g. physicians, physiotherapists) with differing levels of expertise (i.e. novice, intermediate and expert).
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".