Psychometric properties of the French version of the Kogan’s Attitudes toward Older People scale: A cross-sectional study conducted on Cameroonian nursing students
Bibliographic record
Abstract
The Kogan’s Attitudes toward Older People (KOP) scale has been used worldwide to explore nursing students’ attitudes toward older adults. It has various translations with good psychometric properties. The French version of the scale was first used on a Canadian population in 2012, then on a French Cameroonian population in 2020. However, its psychometric properties, especially its factor structure, have never been determined.To determine the psychometric properties of a French version of the KOP scale on Cameroonian French-speaking nursing students.A cross-sectional study was conducted where a self-administered questionnaire in French was given to a convenience sample of 296 nursing students registered for three different nursing programs.The French version of the KOP scale demonstrated moderate psychometric properties. The internal consistency, indicated by the Cronbach’s alpha, was moderate, while the explanatory factor analysis showed two factor loadings, which explained 58.44% of the total variance.Conclusion The French version of the KOP scale can be a useful tool for studies in French-speaking African countries to assess the degree of ageism toward older adults. It is suggested that the original KOP scale be retranslated by African translators and administered to larger French-speaking populations in other countries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".