The Status of a Trainee Teacher with Mental Health Problems: Dilemmas on Inclusion and Exclusion in Higher Education
Bibliographic record
Abstract
The case study reports on a 22-year old female pre-service student teacher pseudo-named P who dropped out of her training programme in Brunei due to psychological problems. Brunei education system has both local and foreign students. For ethical reasons the nationality, ethnicity, and other identifying information of the student are withheld throughout this study. An informal interview and the MMPI-2 evaluation confirmed that P had many severe mental health problems that required a wide range of therapeutic interventions to address. Overall, the present study illustrated how gender, interpersonal relationships, and culture interacted to cause distress for P. In addition the results demonstrated the plight of a tertiary student with mental health problems in a developing country who seemed to be accorded low priority in comparison to peers with other severe disabilities. Moreover, the study also highlighted the importance of psychological assessment in educational counseling and the lack of adequate psychotherapy resources for students with high support needs in mental health. Unless appropriate intervention measures are instituted, the wastage rate among vulnerable students with challenging behaviors might increase in Brunei. Keywords:Psychological; assessment; mental; health; counseling; psychotherapy; education.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".