Bibliographic record
Abstract
This study aims to explore the impact of social support networks on the mental health of gifted students. The study utilized a qualitative research design with a phenomenological approach to capture the lived experiences of gifted students. Twenty-seven participants aged 12 to 18 were recruited from diverse educational settings through purposive sampling. Data were collected using semi-structured interviews, which provided in-depth insights into the participants' perceptions of social support and its impact on their mental health. The interviews were transcribed and analyzed thematically, with themes emerging through iterative coding and constant comparison until theoretical saturation was achieved. The findings revealed that emotional, academic, and peer support are critical for the mental health of gifted students. Family support, characterized by parental encouragement and emotional reassurance, and peer support, through empathetic friendships and social inclusion, were particularly significant. Teacher support also played a crucial role in addressing both academic and emotional needs. However, several barriers to support were identified, including stigma, lack of resources, overemphasis on achievement, peer competition, and reluctance to seek help. These barriers exacerbate feelings of isolation and stress, impacting the overall well-being of gifted students. Social support networks are essential in mitigating the challenges faced by gifted students and promoting their mental health. Creating inclusive and supportive environments in educational settings, addressing barriers, and providing targeted interventions can enhance the well-being of gifted individuals. Future research should focus on longitudinal studies and the intersectionality of giftedness with other demographic factors to develop comprehensive support strategies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".