The What, Why, and How of Adolescent Interpersonal Goal Setting Following a Growth Mindset Intervention
Bibliographic record
Abstract
Interpersonal relationships are central to adolescent well-being. The current research investigates interpersonal goal setting among a general sample of adolescents following a growth mindset intervention. This study qualitatively explores what interpersonal goals adolescents set, outcomes they aim to achieve, obstacles they perceive, and actions to overcome the obstacles during the mental contrasting and the implementation intentions goal setting task (MCII). Participants included 217 grade 9 and 12 students (63.13% White/European). One content and three thematic analyses were conducted on adolescent responses to the MCII. Participants largely set goals related to improving the quantity and quality of their friendships. The ultimate ideal outcome of goal achievement was an improved emotional state. Obstacles were both internal (e.g., characteristics) or external (e.g., others) in nature. Actions identified to overcome the obstacle were either active or passive with passive approaches exhibiting lack of congruence with intervention content. Findings contribute to the empirical understanding of adolescent interpersonal goal setting and provide researchers/practitioners a rich resource of youth experiences to draw on when considering goal setting interventions. A better understanding of adolescents’ lived experiences setting goals also stands to benefit those who seek to aid youth in improving well-being.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".