Technology-Supported Guidance Model to Support the Development of Critical Thinking Among Undergraduate Nursing Students in Clinical Practice: Protocol of an Exploratory, Flexible Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Critical thinking is an essential set of skills in nursing education, and nursing education therefore needs a sharper focus on effective ways to support the development of these skills, especially through the implementation of technological tools in nursing education. OBJECTIVE: The aim of this study protocol is to assess the feasibility of a technology-supported guidance model grounded in the metacognition theory for nursing students in clinical practice. METHODS: Both quantitative (research questionnaires) and qualitative (focus group interviews) approaches will be used to collect data for a feasibility study with an exploratory, flexible mixed methods design to test a newly developed intervention in clinical practice. RESULTS: The intervention development was completed in December 2020. The intervention will be tested in 3 independent nursing homes in Norway. CONCLUSIONS: By determining the feasibility of a technology-supported guidance model for nursing students in clinical practice, the results will provide information on the acceptability of the intervention and the suitability of the outcome measures and data collection strategy. They will also identify the causes of dropout and obstacles to retention and adherence. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31646.
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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 | 0.059 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".