Students With LD and Decision Making for After High School: Emotions and Basic Psychological Needs
Bibliographic record
Abstract
Little is known about how grade 12 students with learning disabilities (LD) face the complex and emotional task of deciding what to do after high school. To expand on current research, we explored the experiences, emotions, and support of Canadian students with LD from the theoretical perspective of basic psychological needs (BPNs). We conducted a triangulation mixed-method study that included survey data and interviews. Participants were asked to identify their emotions, and if their BPNs of autonomy (e.g., feeling in control), competence (e.g., feeling capable), and relatedness (e.g., feeling connected to others) were satisfied or frustrated. Based on survey questions, participants indicated high satisfaction with their BPNs during decision-making and low levels of negative emotions. We used a deductive analysis to link participant interviews to BPNs and found greater satisfaction with these needs than frustration. Moreover, the mixed-method analysis integrating the results in a joint display uncovered that participants identified more positive emotions when their BPNs of autonomy and relatedness were satisfied, and more negative emotions when their BPNs of relatedness and competence were frustrated. We discuss the emotional experience of making decisions after high school. Limitations and future research directions are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".