The Exploration of Critical Care Nurses' Use of Accumulated knowledge and Information-seeking for Non-routine Tasks
Bibliographic record
Abstract
Background\nNurses complete tasks during patient care to promote the recovery, or to maintain the health, of patients. These tasks can be routine or non-routine to the nurse. Non-routine tasks are characterized by unfamiliarity, requiring nurses to seek additional information from a variety of sources to effectively complete the tasks. Nurses’ perception of their problem-solving skills, as characterized by the attributes of personal control, problem-solving confidence, and avoidance-approach style, influences how information is sought.\n \nObjectives/Research Questions\nGuided by the information-seeking behaviour model, this study was designed to: (1) examine how the non-routineness of the task affects nurses’ information-seeking behaviour and the use of accumulated knowledge; and, (2) explore nurses’ perception of their problem-solving abilities.\n\nMethods \nAn exploratory cross-sectional survey design was used. A random sample of critical care nurses who worked in a hospital setting were selected from the College of Nurses of Ontario (CNO) research participant database. Multiple regression analysis was used to examine the proposed relationships.\n\nResults \nAvoidance-approach style and, problem-solving confidence did not have a significant relationship with nurses’ information-seeking behaviour. None of the variables explained use of accumulated knowledge (F = 0.902, p > 0.05). Previous training (p = 0.008), Non-routineness of the task (p = 0.018), and Personal control (p = 0.040) had a positive relationship with information-seeking behaviour (Adjusted R2 = 0.136). \n\nImplications\nThe study results provide evidence that problem-solving ability, and in particular the attribute of personal control, influences nurses’ information-seeking behaviour during the completion of nursing tasks. They reveal how information is sought from resources, and what specific information resources are necessary to promote access to, and use of, evidence-based information. The results also help direct efforts towards training nurses in issues related to problem-solving and information-seeking by targeting the development of personal control and retrieving evidence-based 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".