Curricular Content for Pupils’ Mental Health
Bibliographic record
Abstract
Present-day curricular designs have to take the pupils’ psychological needs in account, thus becoming melodies of mental health and happiness for the next generation. Emphasizing the findings from previous investigations using the research synthesis methodology, the present study has been conducted aiming at achieving some integrative knowledge under the inclusive title of “Mental Health Improvement-based Curricular Content.” Goal-oriented homogeneous sampling method was applied in order to select 100 research papers from Iranian scholars on the subject of pupils’ mental health improvement using accredited databases between 2005 and 2016. Data analysis using open subject coding encompassing three stages, namely open, axial, and selective coding, indicated that a curriculum with contents in two overall categories of mental health literacy (optimism and positive thinking; socialization; monotheistic life; happiness; self-efficacy; self-awareness and self-actualization & …) and mental health skills (emotions management, interpersonal communication, critical thinking, adaptability, tolerance, and finally & ...) has to be designed in order to improve the pupils’ mental health.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".