Techniques for Managing Stress According to the Process of Contemplative Science for Secondary School Students in a Disrupted Classroom Situation
Bibliographic record
Abstract
The objectives of this research were to 1) study the stress levels of secondary school students in a disrupted classroom situation and 2) propose techniques for managing stress according to the process of contemplative science for secondary school students in a disrupted classroom situation. It is action research. The research samples are 1) 300 secondary school students and 2) 9 experts in learning management and contemplative education, totalling 309 people, using the purposive sampling. The research instruments were 1) a stress level questionnaire, 2) a techniques for managing stress quality assessment form, and 3) a group discussion recording form. Quantitative data were analyzed by averaging. and standard deviation and qualitative data was analyzed by content analysis. The results found that. 1. Study the stress levels of secondary school students in a disrupted classroom situation. It was found that from a survey of secondary school students in the upper northern region of Thailand, their stress levels were at the highest level. Which consists of 4 aspects of stress, consisting of 1) academic aspect, 2) economic aspect, 3) social aspect, and 4) family aspect. 2. Propose techniques for managing stress according to the process of contemplative science for secondary school students in a disrupted classroom situation found. The researcher studied research and documents related to the concept of contemplative science and synthesized techniques for managing stress according to the process of contemplative science for secondary school students, it is called Techniques for managing stress according to the process of contemplative science, PATAES model, consisting of 1) Perception 2) Assesses 3) Technique 4) Action, 5) Empathy and 6) Support had the highest level of results for evaluating the quality of appropriateness.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".