Ottawa Decision Support Framework to Improve Iranian Nurses’ Decision Coaching Skills
Bibliographic record
Abstract
Introduction: Nurses play an important role in providing decision coaching (DC) and developing informed decision-making in families of patients hospitalized in intensive care units (ICUs).Therefore, taking necessary measures to develop nurses' DC skills is essential.The present study was conducted to analyze the application of the Ottawa Decision Support Framework (ODSF) in developing Iranian nurses' DC skills.Methodology: In this experimental pretest-posttest study, two hospitals (Imam Reza and Shahid Kamyab hospitals) in the city of Mashhad were randomly placed in either the experimental or the control group.Based on a simple random sampling method, 60 ICU nurses were selected.For nurses in the experimental group, a 2-day workshop was conducted based on the ODSF, whereas nurses in the control group received no intervention.Using the SPSS-16 software and statistical tests of paired-samples t-test, independent-samples t-test, and Chi-square test (p <0.05), the data were analyzed.Results: Before the intervention, no difference was observed in the mean DC scores obtained by the nurses in the experimental and control groups (p = 0.891).However, after the intervention, a significant difference was observed in the mean DC scores obtained by the nurses in the experimental and control groups (p <0.001). Conclusion:The results indicated that applying the ODSF is effective in improving Iranian nurses' DC skills.It was also indicated that the concepts presented in this framework are consistent with Iranian nurses' cultural backgrounds.Accordingly, the application of the ODSF is offered in Iranian nurses' continuing education programs to improve their DC skills.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".