Investigation of the Relationship Between Adolescents’ Career Indecision, and Social Support Perception and Basic Psychological Needs
Bibliographic record
Abstract
The aim of this study is to examine the relationship between high school students’ emotional and personality-related career decision-making difficulties (EPCD), perception of social support and basic psychological needs. The predictive effect of social support perception and basic psychological needs on EPCD will also be examined. In addition, it has been examined whether emotional and personality-related career decision-making difficulties differ according to some demographic variables. In this research, survey model was used. For collecting data, Personal information form; the EPCD-short form; New Psychological Needs Assessment Scale, were used. The data obtained in this research were analyzed using SPSS (20). In the analysis of the data, descriptive statistics, independent groups t-test, ANOVA test, and correlation and regression analysis technique were used. In addition, a significant correlation was found between the mean scores of the students on the career difficulties, and the perceived social support, and the autonomy and the achievement. The findings were discussed as a part of the relevant literature and suggestions based on the findings were included.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".