HAPPINESS PERCEPTIONS OF PRESCHOOLERS AND PARENTAL STRATEGIES USED TO INCREASE CHILDREN’S HAPPINESS
Bibliographic record
Abstract
This qualitative research study aimed to evaluate how preschoolers aged 4 to 5 perceived “happiness” and the strategies parents use to make their children happier. The criteria sampling approach, a purposeful sampling technique, was utilized to create the study group, which included 20 preschoolers and their parents who had applied for assessment by the Child Development Polyclinic of a maternity and pediatrics hospital in Istanbul, Türkiye. Apart from demographic information, the data employed in the study comprised semi-structured interviews and children’s descriptions of their drawings; these were analyzed using content analysis. The study’s findings showed that the children used adjectives that express positive emotions, such as “laughing”, “joyful”, and “loving”, to describe their feelings, and were reported being happy when they engaged in play-based activities such as playing video games, watching television, engaging in sports, and visiting the beach or the park. The children’s picture drawings of happiness mostly featured aspects of play and nature. One of the research’s most significant results is that parents employed a variety of methods to make their children happy, like playing games, taking them on walks, taking them to the park, and cooking their desired foods. The article concludes with suggestions based on the findings obtained from the research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".