Evaluating knowledge and attitudes scales for the care of older adults among nursing students in Ghana
Bibliographic record
Abstract
Abstract Background Understanding nursing students’ knowledge about and attitudes toward older adults’ using context-specific survey instruments can help to identify and design effective learning and teaching materials to improve the care for persons 60 years and above. However, there are no validated instruments to examine nursing students’ knowledge and attitudes toward the care for older adults in the African context. The study aimed to evaluate the items on the Knowledge about Older Patients Quiz and Kogan's Attitudes towards Old People Scale suitable for the African context. Methods A cross-sectional study was conducted using second-and third-year nursing students from two public Nursing Training Institutions in Ghana. Using Sahin’s rule of sample size estimate of at least 150 participants for unidimensional dichotomous scales, 170 nursing students were recruited to participate after an information session in their classrooms. Data were collected from December 2019—March 2020 using the Knowledge about Older Patients Quiz and Kogan’s Attitudes Towards Old People Scale. Item response theory was employed to evaluate the Knowledge about Older Patients Quiz difficulty level and discrimination indices. Corrected item-to-total correlation analysis was conducted for Kogan's Attitudes towards Old People Scale. The internal consistency for both scales was examined. Results Of the 170 participants, 169 returned completed surveys. The mean age of participants was 21 years (SD = 3.7), and (54%) were female. Of the 30-items of the Knowledge about Older Patients Quiz, seven items were very difficult for most students to choose the correct response, and one was easy, as most of the students chose the correct response. Although 22 items demonstrated appropriate difficulty level, discrimination indices were used to select the final 15- items that discriminated moderately between upper and lower 25% performing students. The Kuder-Richardson-20 reliability was. 0.30, which was low. Considering Kogan's Attitudes towards Old People scale, 10-items were removed following negative and low corrected item-to-total correlation and a high Alpha coefficient if items were deleted. The final 22-items had a Cronbach alpha coefficient of 0.65, which was moderately satisfactory. Conclusion Evaluation of the scales demonstrated essential content validity and moderate internal consistency for the context of our study. Further research should focus on ongoing context-specific refinement of the survey instruments to measure nursing students’ knowledge about and attitudes toward caring for older adults in the African context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".