Towards an Afghan counselling psychology: A partnership to integrate psychological counselling into the university curriculum at Afghanistan’s flagship public universities
Bibliographic record
Abstract
Developing sustainable efforts to address the psychosocial consequences of complex emergencies is often a challenge. There is a limit to what humanitarian efforts can achieve, even with the best of intentions. Locally based tertiary education programmes are needed to provide conceptual frameworks and to develop and sustain professional psychosocial support programmes both during and after the emergency. In Afghanistan, over 30 years of armed conflict and its sequelae have placed an extraordinary emotional strain on every member of the population, affecting not only individuals but also families and communities. The Ministry of Higher Education has therefore taken the initiative to develop two new academic programmes in counselling psychology in its public university system. These programmes are designed to prepare a new generation of academics who can come to understand the specific Afghan context of psychosocial suffering and prepare appropriate interventions to support transformation on an individual and communal level. They are also poised to educate a new generation of qualified practitioners to serve individuals, families, communities and the society itself. In this article, the authors describe a partnership programme with the two universities designed to provide external support for their efforts.
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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 | 0.021 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 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".