Recovery-oriented social work practice in mental health and addictions: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Social work is a key profession in the field of mental health worldwide and the profession has values that are aligned with a recovery paradigm. However, there are gaps in understanding how social workers are applying the recovery paradigm in practice. This study will scope and synthesise the literature related to recovery and social work practice in mental health and addictions. There will also be an exploration of best practices and gaps in recovery-oriented social work practice. METHODS AND ANALYSIS: Using a scoping review framework developed by Arksey and O'Malley, we will conduct our search in five academic databases: PsycINFO, Medline, CINAHL Plus, Sociological Abstracts and Social Services Abstracts. Articles meeting inclusion criteria will be charted to extract relevant themes and analysed using a qualitative thematic analysis approach. ETHICS AND DISSEMINATION: This review will provide relevant information about best practices and gaps in recovery-oriented social work practice in mental health and addictions. The study will inform the development of mental health curricula in social work programmes and clinical settings. Results will be disseminated through a peer-reviewed journal and at conferences focusing on mental health, addictions, and social work education. Ethics approval is not required for this scoping review.
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 | 0.114 | 0.082 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".