Transformative Mental Health Social Work Practice: What, When, and with Whom Do We Learn?
Bibliographic record
Abstract
Purpose: Transformative mental health social work fosters participatory processes towards emancipatory changes in services and systems, addressing societal barriers to inclusion, equity, and full citizenship. This study examined how transformative practices aligned with United Nations’ calls for change/reform toward person-centered, rights-based mental health recovery across both Canada and Australia, are learned. Method: Using co-operative inquiry, the study captured diverse experiences and knowledge from social work practitioners, managers, students, academics, and family members in a practice research partnership. We sought to examine how learning prepares and sustains mental health social workers for transformative practice. Results: The study revealed an iterative approach to learning in mental health and highlighted the diverse foci of learning (what) at various career stages (when) and the reciprocal nature of learning for and from others in the practice environment (from whom). Discussion: In this unique context, the importance of professional identity resilience was underscored.
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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".