Scope, quality and inclusivity of international clinical guidelines on mental health and substance abuse in relation to dual diagnosis, social and community outcomes: a systematic review
Bibliographic record
Abstract
OBJECTIVE: It is estimated that up to 75% of patients with severe mental illness (SMI) also have substance use disorder (SUD). The aim of this systematic review was to explore the scope, quality and inclusivity of international clinical guidelines on mental health and/or substance abuse in relation to diagnosis and treatment of co-existing disorders and considerations for wider social and contextual factors in treatment recommendations. METHOD: A protocol (PROSPERO CRD42020187094) driven systematic review was conducted. A systematic search was undertaken using six databases including MEDLINE, Cochrane Library, EMBASE, PsychInfo from 2010 till June 2020; and webpages of guideline bodies and professional societies. Guideline quality was assessed based on 'Appraisal of Guidelines for Research & Evaluation II' (AGREE II) tool. Data was extracted using a pre-piloted structured data extraction form and synthesized narratively. Reporting was based on PRISMA guideline. RESULT: A total of 12,644 records were identified. Of these, 21 guidelines were included in this review. Three of the included guidelines were related to coexisting disorders, 11 related to SMI, and 7 guidelines were related to SUD. Seven (out of 18) single disorder guidelines did not adequately recommend the importance of diagnosis or treatment of concurrent disorders despite their high co-prevalence. The majority of the guidelines (n = 15) lacked recommendations for medicines optimisation in accordance with concurrent disorders (SMI or SUD) such as in the context of drug interactions. Social cause and consequence of dual diagnosis such as homelessness and safeguarding and associated referral pathways were sparsely mentioned. CONCLUSION: Despite very high co-prevalence, clinical guidelines for SUD or SMI tend to have limited considerations for coexisting disorders in diagnosis, treatment and management. There is a need to improve the scope, quality and inclusivity of guidelines to offer person-centred and integrated care.
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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 | 0.055 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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".