Responding to student mental health concerns in social work education: reflective questions for social work educators
Bibliographic record
Abstract
In this paper, we explore ways in which social work educators might respond to students who report that mental health issues underlie their difficulty in meeting core competencies, or otherwise use the language of mental health to describe their struggles to succeed in social work programs. We discuss various trends in policy responses in Canada, the US, the UK, and Ireland. While there are general policy trends, it is clear that responding to these kinds of issues requires the development of highly flexible and situated policy processes that can respond to student realities, concern for students’ rights and privacy, and an awareness of potential discrimination against students. These processes also need to meet the specificities of practicums, particular institutional policies, the mandates of relevant professional bodies, and the precise local legislative framework that shapes these situations. Given these varying contexts, in this conceptual paper, we used a framework on disability that is informed by critical theory to engage existing school policies and propose a set of reflective questions that can guide schools of social work to create an overall responsive environment. These reflective questions are designed to help social work educators balance the rights and needs of students with the professional and institutional demands that students meet core competencies in their 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 | 0.119 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.058 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.017 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".