Examining diagnosis as a component of Social Workers’ scope of practice: a scoping review
Bibliographic record
Abstract
Abstract Our study reports on a scoping review examining the role of diagnosis within social work practice in Canada and the United States (US). Adopting the process laid out by Arksey and O’Malley, the search captured 189 academic and grey literature drawn from six health and social sciences databases and published from January 1980 to April 2020. The majority of literature were based in the US, and studies covered a variety of practice settings including hospitals, community clinics, medical health clinics, and private practice. Various versions of the DSM was mentioned in over half of the publications that made mention of the use of a diagnostic tool. Four themes emerged from the analysis: professional positioning, clinical activities and diagnosis, contextual factors, and education and training. The focus on holistic care through intersecting identities and social determinants of health in social work provides a balance to the biomedical model adopted by the DSM. Further inclusion of social work perspectives in the development of the DSM may help raise awareness and inclusion of ecological factors in diagnosis. In addition, the lack of uniformity in the inclusion of diagnosis in social work curriculum has been noted as an opportunity to offer higher quality instruction and supervision to students to better utilize diagnostic tools. We suggest that expanding the scope of practice for social work to include diagnosis can help increase the capacity of the healthcare system to identify and address mental health concerns.
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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 | 0.037 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".