Social work education and training in mental health, addictions and suicide: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Social workers are among the largest group of professionals in the mental health workforce and play a key role in the assessment of mental health, addictions and suicide. Most social workers provide services to individuals with mental health concerns, yet there are gaps in research on social work education and training programmes. The objective of this scoping review is to examine literature on social work education and training in mental health, addictions and suicide. METHODS AND ANALYSIS: Using a scoping review framework developed by Arksey and O'Malley, we will search for literature through seven academic databases: PsycINFO, Sociological Abstracts, CINAHL Plus, Social Sciences Abstracts, Education Source, ERIC and Social Work Abstracts. Two independent reviewers will screen articles utilising a two-stage process. Titles and abstracts will be reviewed in the first stage and full texts will be reviewed in the second stage. Selected articles that meet inclusion criteria will be charted to extract key themes and they will be analysed using a qualitative thematic analysis approach. ETHICS AND DISSEMINATION: This review will fill a knowledge gap in social work education and training in mental health, addictions and suicide. Ethics approval is not required for this scoping review. Through dissemination in publications and relevant conferences, the results may guide future research and education in social work.
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 | 0.108 | 0.072 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.014 |
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".