Evaluating the Knowledge about Older Patients Quiz and the Kogan's Attitudes towards Old People Scale for Nursing Students' Gerontological Learning Experiences in Ghana.
Bibliographic record
Abstract
Abstract Background: Understanding nursing students' knowledge about and attitudes towards older adults' using a context-specific survey instrument may help to identify, and design effective learning and teaching materials to improve the care for older adults. The study aimed to evaluate the items on the Knowledge about Older Patients Quiz and the 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. Data were collected from December 2019 - March 2020 using the Knowledge about Older Patients Quiz and the Kogan's Attitudes Towards Old People Scale. Item response theory was employed to evaluate the difficulty level and discrimination indices of the Knowledge about Older Patients Quiz. Corrected item-to-total correlation analysis was conducted for the 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 21years (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 of the 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. Considering the 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. 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 towards the care of older adults in the African context.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".