Effects of Computer-Based Clinical Conferencing on Nursing Students’ Self-Efficacy
Bibliographic record
Abstract
A pretest-posttest, quasi-experimental, control group design and Bandura's theory of self-efficacy were used to examine the influence of computer conferencing on fourth-year baccalaureate nursing students' self-efficacy for professional nursing competencies and computer-mediated learning (CML) during a final clinical practicum. Descriptive analysis was also used to explore themes regarding strengths and challenges of online learning. The convenience sample included 42 direct-entry students (control group: n = 27; online intervention: n = 15). Within both groups, there was a significant difference in self-efficacy for nursing competencies from pretest to posttest. However, between-group posttest scores were not significantly different. Computer conferencing enhanced learning, and students' self-efficacy for CML increased at posttest. Strengths of CML included connection, support, learning, and sharing. Challenges involved time and Internet access. Insights gained may assist educators in curriculum development when considering how CML strategies support clinical courses and strengthen learning communities.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".