Needs Assessment to Develop Online Counseling Program
Bibliographic record
Abstract
Undergraduate students are always faced with diverse mental health problems. Nowadays, they can easily access online counseling services to reduce their problems. This research determines the most desired component to develop the online counseling program which aims 1) to study the needs to develop an online counseling program, 2) to compare the needs to develop this program by categorizing with gender, year, academic program, and grade point average (GPA), and 3) to rank the priority needs for developing this program. Participants were 416 undergraduate students who studied in Mahasarakham University and were selected by stratified random sampling. The research instrument was the needs assessment questionnaire to develop this program. The data were analyzed using percentage (%), mean (M), standard deviation (S.D.), independent sample t-test, one-way ANOVA, and modified priority needs index (PNIModified). The results revealed that: 1) the mean of actual condition was at a high level (M = 3.76, S.D. = 0.59) and the mean of the desired condition was at the highest level (M =4.50, S.D.= 0.56) with the significant difference at 0.05 level in all four domains. 2) Undergraduate students from different academic programs exhibited the marked different means of needs for the online counseling program. The scores on actual conditions among students of Mahasarakham Business School were higher than students from others. 3) Students showed the highest priority needs on characteristics of counselor and online application for counseling (PNIModified = 0.203). They showed that the secondary needs on characteristics of online counselee (PNIModified = 0.192) and therapeutic relationship after online counseling (PNIModified = 0.177).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".