Bibliographic record
Abstract
Objective – To describe critical care nurses’ on-duty information-seeking behavior. Design – Participatory action research using ethnographic methods. Setting – A twenty-bed critical care unit in a 275-bed community (non-teaching) hospital. Subjects – A purposive sample of six registered nurses (RNs) working shifts in the critical care unit. Methods – The researcher accompanied six RNs on various shifts (weekdays and weekends, day and night shifts) in the critical care unit and used participant observation and in-context interviews to record fifty hours of the subjects’ information-seeking behavior. Transcripts were written up and checked by the subjects for accuracy and validity. The resulting rich data was analyzed using open coding (concepts which emerged during data gathering, for example “nurse’s personal notes”); in vivo coding (participant-supplied concepts, for example “reading on duty”); and axial coding (hierarchical, researcher-developed concepts such as “information behaviors, information sources, information uses, and information kinds”) (147). Main results – The critical care nurses constantly sought information from people (patients, family members, other health care workers), patient records, monitors, and other computer systems and noticeboards, but very rarely from published sources such as books or online databases. Barriers to information acquisition included equipment failure, illegible handwriting, unavailable people, social protocols (for example physician – nurse interaction), difficult navigation of computer systems, and mistakes caused by simultaneously using multiple complex systems. Conclusion – Critical care nurses’ information behavior is strongly patient-centric. Knowledge-based information sources are rarely consulted on duty due to time constraints and the perception that this would take time away from patient care. In seeking to meet the knowledge-based information needs of this group, librarians should be wary of traditional, academic models of information delivery. Instead, they should consider a tailored ready reference service incorporating quality and quantity filtering.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| 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".