Enhancing Student Counseling Management in Vocational Undergraduate Colleges: Development of an Integrated Approach in Nanchang, China
Bibliographic record
Abstract
This study developed an integrated approach to student counseling management in vocational undergraduate colleges in Nanchang, China. Using a sequential mixed-methods design, the research comprised three phases: (1) identifying counseling components through document analysis and expert validation (n = 8); (2) assessing current and desired states via surveys with 135 stakeholders from two colleges; and (3) developing an approach based on findings. Six components with 48 indicators were validated: academic guidance, professional and technical guidance, emotional and psychological counseling, career planning, social counseling, and life counseling. Significant gaps existed between current state (X̅ = 2.52) and desired state (X̅ = 4.89). Priority needs analysis identified emotional and psychological counseling (PNImodified = 1.03), social counseling (PNImodified = 0.99), and academic guidance (PNImodified = 0.94) as most critical. The developed approach integrated the 70:20:10 learning model, emphasizing experiential learning, social learning, and formal training. Expert evaluation confirmed high suitability (X̅ = 4.80) and feasibility (X̅ = 4.30). This research provides the first empirical framework for counseling management in Chinese vocational undergraduate education, addressing urgent mental health support needs while maintaining vocational education’s practical orientation. The validated approach offers institutions a systematic improvement roadmap with recommendations for phased implementation prioritizing psychological services and industry partnerships.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".