Social work’s scope of practice in the provision of primary mental health care: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Social work is a key member of interprofessional primary healthcare teams and foundational to primary healthcare reforms that aim to improve the provision of mental healthcare. Little is known, however, about social work's scope of practice within primary healthcare settings, particularly in the provision of mental healthcare. The objective of this study is to identify and describe social work's scope of practice as it relates to mental healthcare in primary healthcare settings. METHODS AND ANALYSIS: A scoping review will be conducted using the methodology established by Arksey and O'Malley. We will search electronic databases (MEDLINE, Embase, PsycINFO, CINAHL, Social Services Abstracts and Social Work Abstracts) to identify studies appropriate for inclusion. One reviewer will independently screen all abstracts and full-text studies for inclusion, with supervision by lead investigator. We will include any study that focuses on social work and mental healthcare within primary healthcare settings. All bibliographic data, study characteristics and range of social work practice activities will be collected and analysed using a tool developed by the research team. ETHICS AND DISSEMINATION: The scoping review will synthesise social work's scope of practice in the provision of mental healthcare within primary healthcare settings. This review will be the first step to formally develop guidelines for social work practice in primary healthcare. The results will be disseminated through a peer-reviewed publication and conference presentations.
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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 | 0.137 | 0.110 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.060 | 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".