Bibliographic record
Abstract
School experiences significantly mold and shape children’s developmental well-being and are therefore intended to be a holistic defining experience. However, in South Africa there is an escalating prevalence, nature, and impact of challenges that young people present within a school’s setting that hinders academic learning and teaching. Psychosocial interventions in schools have the potential to advance and support adolescent mental health and well-being and that of their teachers as well. School-based psychosocial services are seen as a promising intervention for learners presenting with psychosocial challenges and towards prevention, identification, and management of crises experienced in school settings. Many schools in Africa have psychosocial services in place. However, the documentation of such programs and their effectiveness is limited, highlighting the need for further research to inform practice and policy. To date, all studies on psychological services in South Africa have argued that psychosocial support improves learners’ well-being, their access to education, and other opportunities. This study sought to gain further insight into the experiences of school psychosocial practitioners on interventions with adolescents. Findings from this study add to the growing body of literature and argue that psychosocial specialists in school settings working with young adolescents should strive to use varied methods of intervention. Teachers and psychosocial specialists need to work collaboratively to maximize their impact on learners’ achievements. In addition, young adolescents learn best and develop optimally when learning and other in-school interventions are integrated. Lastly, psychosocial specialists are an excellent resource in assisting teachers in managing learners and coping with their own individual stressors emanating from the learners and their parents/caregivers.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".