Feedback Survey for an Online Learning Module: Developing and Validating a Scale to Measure Nursing Students' Self‐Assessed Knowledge and Perceptions of Older People and Confidence in Working With Them
Bibliographic record
Abstract
PURPOSE: To determine if an online learning module on older people's care improved nursing students' self-assessed knowledge, and perceptions of older people, we developed a brief Feedback Survey. The aim of this study was to examine the internal consistency (a type of reliability) and construct validity of the feedback survey. DESIGN AND METHODS: Secondary analysis of data from the Awakening Canadian's to Ageism and McCalla e-learning intervention studies for postsecondary nursing students. Factor analysis and reliability analysis (via standardised Cronbach's alpha) were performed on the four-question, five-point Likert-type Feedback Survey, which was included in both intervention studies. RESULTS: Factor analysis yielded one factor interpretable as general satisfaction in students' experience with the module and perceived benefits of having completed it. Standardised Cronbach's alpha for this scale was high at 0.92, which suggests excellent internal consistency. IMPLICATIONS FOR PRACTICE: The feedback survey is a convenient and time-efficient measure to examine student nurses' self-assessed improvements in knowledge, perceptions about older people. The survey has potential for adaptation to measure perceived outcomes of other nursing student- focused education.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".