Effect of A Resilience Programme Through Group Dynamics on the Academic Problems of Grade 7 Students, Chiang Mai University Demonstration School
Bibliographic record
Abstract
Background: Early adolescents may encounter adverse situations that could cause stress and anxiety. To prevent mental health problems and promote mental health, resilience should be promoted in early adolescents. Objectives: The objectives of this study were to develop a resilience program and evaluate the effectiveness of such a programme on the academic problems of Grade 7 students of the Chiang Mai University Demonstration School, Chiang Mai, Thailand. Methods: This quantitative research used a quasi-experimental design, which was a controlled study with a pretest and post-test. The purposive selection was used to choose 70 participants from the Grade 7 students, the academic year 2021 from the Chiang Mai University Demonstration School. The criteria were students who had the lowest resilience inventory score from the student population. The participants were classified with stratified random sampling into the experimental groups (n=35), who received a resilience program through the group’s dynamics once a week for 11 weeks at 60 minutes each time, and the control groups (n=35), who received a resilience knowledge sheet and had a normal life. Two participants in each group withdrew, leaving a total of 33 participants per group. The data were collected by assessing before and after participating in the programme with a general information questionnaire, the Canadian Occupational Performance Measures (COPM), and Resilience Inventory. Descriptive statistical analysis, an independent t-test, and paired t-test were used to analyse the data. Results: This resilience programme had an overall consistency index of 0.78. After treatment, the experimental group and control group had a statistically significant difference in the pretest and post-test resilience scores. (p < 0.01 and p < 0.05). The experimental group had pretest and post-test significant differences in academic performance and satisfaction (p < 0.01), while the control group had no statistically significant difference. There was a statistically significant difference in the post-test academic satisfaction between both groups. (p < 0.01), while academic performance displayed no statistically significant difference. Conclusion: This resilience programme, through the groups’ dynamics could contribute to the resilience, academic performance, and academic satisfaction on academic problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".