Mental Health Social Work Practice: Navigating Tensions
Bibliographic record
Abstract
Abstract The United Nations Special Rapporteur has called for ‘a revolution in mental health care to end decades of neglect, abuse and violence…enabl[ing] a long overdue shift to a rights-based approach’ (United Nations Human Rights Office of the High Commissioner, 2017). Principles underpinning social work practice with people living with mental distress who receive diagnoses such as schizophrenia, personality disorders and bipolar disorder, align with sought after changes. Using Cooperative Inquiry and referring to the Model of Holistic Competence in Social Work, this study investigated mental health social work seeking to foster this shift. Findings confirmed inherent discordance between social work values and mental health care dominated by biomedical and neoliberal views. Upholding social work values in this context involves complex practice, integrating procedural and meta-competencies. By incorporating skills in relational practice and advocacy; knowledge of multiple explanatory frameworks for mental health and mental health systems literacy; self-regulation; fostering tenacity whilst addressing ‘othering’; and judgement informing choices around conformity and confrontation, social work navigates tensions associated with this discordance. This paper highlights the nature of values-based social work practice as aligned with mental health reforms.
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 | 0.049 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.028 | 0.049 |
| Scholarly communication | 0.030 | 0.019 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".