A Qualitative Study of Goal-Striving in Adolescents with ADHD
Bibliographic record
Abstract
Setting and attaining goals is linked to many positive outcomes for youth, but not all youth are successful in goal pursuit–particularly in the context of having ADHD. Repeated goal ‘failure’ tends to increase engagement in health-risk behaviors and often has deleterious implications for future educational and vocational endeavors (e.g., higher rate of school drop-out, chronic underemployment). This study had two objectives: first, to identify similarities and differences in goal setting and goal pursuit in youth with ADHD ( n = 10; M age = 14.6 years; SD = 1.3) and typically developing youth (n = 20; M age = 15.6 years; SD = 1.3) and second, to compare goal striving in both groups of adolescents to that of their emerging adult peers ( n = 22; M age = 19.77 years; SD = 1.3). Semi-structured interviews were conducted and iterative thematic analysis was utilized to identify themes. Themes shared across groups highlighted reasons for, resources toward, and stressors associated with goal pursuit. Compared with the other groups, however, adolescents with ADHD applied more effort and allocated more strategies even when goals were of high interest (e.g., hobbies), rated academic goals as less interesting even when successfully attained, endorsed using fewer executive functions (e.g., planning, organizing) during goal pursuit, and did not link immediate goals to future ambitions. Our qualitative study provides a voice for youth with ADHD regarding their experience of goal-striving and offers a perspective for clinicians, caregivers, and educators to consider when working with adolescents who have this relatively common neurodevelopmental disorder.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".