Evaluation of the psychometric properties of the nursing students’ attitudes toward mental health nursing and consumers instrument
Bibliographic record
Abstract
The present study aimed to evaluate the psychometric properties of the instrument “Nursing Students’ Attitudes Toward Mental Health Nursing and Consumers” for use among Nursing students in Brazil. The subjects were 91.3% female and 8.7% male and their ages ranged from 18 to 58 years, with a mean (M) of 21.9 years and a standard deviation (sd) of 3.88 years. The study included students from the undergraduate course in Nursing of five higher public education institutions, with a total of 393 students. Of these, 365 answered the questionnaires at their two application times, resulting in 92.87% full participation in the study. Regarding the school year, 23.9% were from 2013 and 76.1% from 2014. The data were entered into the Statistical Package for the Social Sciences (SPSS) program, v.21, to be analyzed by descriptive exploratory analysis, correlations analysis, means comparison and Principal Component Analysis (PCA) with the exploratory factor analysis technique and oblique rotation. Convergent validity was used as the form of validation, using the Authoritarianism and Minority View dimensions of the Opinions about Mental Illness (OMI) Scale. After generating the structure of the instrument to be tested, the Confirmatory Factor Analysis technique (CFA) was used, through the Structural Equation Modeling technique (SEM), with AMOS/SPSS, v.22, to identify and specify the model, through Maximum Likelihood (ML) estimation. The fit was given by the X2 model, and the absolute, incremental and parsimonious fit indices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".