Latent profile analysis and its influencing factors of alexithymia among nursing students in vocational
Bibliographic record
Abstract
ObjectiveTo explore the potential categories of alexithymia among nursing students in vocational and to analyze their influencing factors.MethodsA total of 828 nursing students from the 2022 and 2023 vocational colleges in Anhui province were selected as the research subjects by convenience sampling from October to November 2023.General information survey,Chinese version Toronto Alexithymia Scale,and Chinese version Interpersonal Reactivity Index⁃C were used for the survey.The potential classification of alexithymia among nursing students was explored by latent profile analysis,and the optimal classification was determined.The influencing factors of the potential categories of alexithymia was explored by Logistic regression analysis.ResultsAlexithymia among nursing students in vocational could be divided into 3 categories,included 62 individuals(7.5%) in the non⁃expressive disorder or low⁃risk group,351 individuals(42.4%) in the moderate risk group,and 415 individuals(50.1%) in the high⁃risk group.The Logistic regression analysis results showed that family residence,reasons for choosing nursing majors,and empathy ability were potential influencing factors of the categories of alexithymia among nursing students in vocational(P<0.05).ConclusionsThere is heterogeneity in the alexithymia among nursing students in vocational.Nursing educators and educational managers should promptly identify nursing students in vocational with alexithymia,balance the development of empathy ability,and reduce the level of alexithymia.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".