Describing Telenurses' Decision Making Using Clinical Decision Support: Influential Factors Identified.
Bibliographic record
Abstract
OBJECTIVE: Understand the cognitive processes of telenurses' decision making with the use of health information systems (HIS), specifically Clinical Decision Support Systems (CDSS). In addition, identify the factors that influence how telenurses use CDSS. METHODS: Eight telenurses were recruited to manage two call scenarios in a clinical simulation. The call encounters were video recorded and the phone calls were audio recorded. The screens were also recorded to capture the HIS navigation. After the call was completed, the recordings were played back for the telenurse and discussion ensued regarding any issues with the system; this encounter was also recorded for further analysis. RESULTS: Several factors were identified that influenced how telenurses made decisions while using the CDSS. It was found that the decision ladder model could be applied to describe telenurse strategies while using CDSS. The purpose of this paper is to describe the emerging factors that influence telenurses' decision making during a clinical simulation study in a telenursing call centre.
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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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".