Developing Psychological Well-Being Scale for Preschool Children
Bibliographic record
Abstract
The aim of this research was to develop a scale in order to measure psychological well-being in preschool children. Three hundred and seventy five to six year old children participated in the research from 5 regions of Tehran, using accidental sampling method. The participants were individually interviewed with the Well-Being in Preschool Children Scale, and their teachers completed Rutter’s Child Behavior Questionnaire about each of them. Data was analyzed with both exploratory and confirmatory factor analysis methods using WLSMV and GEOMIN oblique rotation, to examine factorial structure. Samejima’s graded response model was used to access psychometric features of the items. Test-retest reliability was measured and Pearson’s correlation was also used to assess divergent and convergent validity. Findings revealed that this scale has 3 main factors: self-concept, life satisfaction and resilience. The validity and reliability of the scale is also satisfactory. The well-being indicators in this scale are consistent with previous research on components of well-being in children. In addition there is a negative correlation between psychological well-being and behavioral problems, which is also illustrated in previous research.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".