Coping Mechanism of the Humss Senior High School Students of St. Paul University Surigao in the New Normal
Bibliographic record
Abstract
Presented in this study is the Coping Mechanism of the HUMSS Senior High School Students of St. Paul University Surigao in the New Normal. The factors that help determine the coping mechanism of the students were emotion-focused and problem-focused. There were 75 Grade 11 and 121 students from the Grade 12, as participants of the study using the purposive random sampling technique. Each participant was sent a link to a researchers-made questionnaire using Google form as a medium of the survey. The data gathered were analyzed by the following descriptive statistical tools: (a) frequency distribution and percentage, (b) mean and standard deviation, (c) analysis of variance (ANOVA).With this, the coping mechanism when it comes to emotion-focused had an average of 3.19 and was described as often. The coping mechanism when it comes to problem-focused had an average of 3.09 and was described as often. In the view of the findings and the conclusion drawn from the gathered data about what coping mechanism of the HUMSS students, the emotion-focused has been found often when it comes to the student’s coping mechanism while as to problem-focused, students still obtained it as a coping when facing and easing their tension. Also, the student’s coping mechanism when using emotion-focused and problem-focused vary according to their sex.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".