Gifted children: What and how to care?
Bibliographic record
Abstract
Gifted children may have a greater risk of experiencing mental health and socio-behavioural issues than the general child population. The present study investigates the protective and risk factors for the mental health and sleep patterns of gifted children and aims to determine if Meaning Mindset (MM), Mental Wellbeing (MWB), Sleep Patterns, Social Support, and Strengths and Challenges among gifted children are interrelated. Participants (N=17, 12 boys, 5 girls, Mage = 11.50 years, SD = 3.78 years) are gifted children (IQ ≥130) recruited through 25 Facebook pages for parents of gifted children across Canada, Association for Bright Children branches across Ontario, and an association for Gifted Education. Primary caregivers provided ratings of child sleep patterns and disruptions using a sleep log and descriptions of their children’s Gifted Strengths and Challenges (GSC). Demographic questions were also asked. Gifted children provided ratings of their MM and their MWB. Results from this study indicated that children’s IQ scores were significantly inversely associated with MM, positively associated with frequency of nighttime awakenings and with GSC, but, notably, do not suggest any significant relationships with total sleep duration and MWB. Further, GSC was inversely associated with MWB and MM, as well as positively associated with IQ score and night awakenings. Notably, the findings from this study also highlight the moderating role of GSC not only in the relationship between child sleep duration and MM but also in the relationship between total sleep duration and child-reported MWB. Additionally, a mediation analysis revealed the mediating role of MM in the relationship between GSC and MWB. These results provide valuable insights into both the protective and risk factors that could potentially influence the well-being of gifted children.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".