PROTOCOL: The effectiveness of abstinence‐based and harm reduction‐based interventions in reducing problematic substance use in adults who are experiencing severe and multiple disadvantage homelessness: A systematic review and meta‐analysis
Bibliographic record
Abstract
Background: Homelessness is a major social and public health concern. It is a traumatic experience, and can have a devastating effect on those experiencing it. People who are homeless often face significant barriers when accessing public services, and those experiencing more visible and extreme forms of homelessness have often faced adverse childhood events, extreme social disadvantage, physical, emotional and sexual abuse, neglect, low self-esteem, poor physical and mental health, and much lower life expectancy compared to the general population. Problematic substance use is disproportionately high amongst people experiencing homelessness, with many using drugs and alcohol to deal with the stress of living on the street, to keep warm, or to block out memories of previous abuse or trauma. Drug overdose is a major cause of death for people experiencing street homelessness. Substance dependency can also create barriers to successful transition to stable housing. There is ongoing policy interest in the effectiveness of different interventions that aim to stop, reduce or prevent problematic substance use, and there is specific interest in the relative effectiveness of interventions that adopt harm reduction or abstinence-based approaches. Objectives: The objective of this review is to understand the effectiveness of different substance use interventions. The review will consider the effectiveness of harm reduction-based interventions, and abstinence-based interventions, for adults experiencing homelessness. The focus of the review is on high-income countries. Search Methods: The primary source of studies for potential inclusion in this review is the Homelessness Effectiveness Studies Evidence and Gaps Maps (EGM). The first of these was published in 2018, with updates published in 2019 and 2020. A further update is due to be published in the summer of 2022. It is this update that provides the final list of studies from which this review will draw. The search for this update (EGM 4th edition) was completed in September 2021. Other potential studies will be identified through a call for grey evidence and hand-searching key journals. Selection Criteria: Eligible studies will be impact evaluations with designs at levels, 3, 4 and 5 of the Maryland Scientific Methods scale. This therefore includes all studies categorised as either 'Randomised Controlled Trials' or 'nonexperimental designs with a comparison group' from the studies which form the basis of the Homelessness Effectiveness Studies Evidence and Gap Maps (EGM) created by CHI and the Campbell Collaboration. We are interested in studies that examine the effect of interventions on substance use outcomes. Studies to be excluded are those with designs at levels 1 and 2 of the Maryland Scientific Methods scale, for example, studies without a control or comparison group, 'before vs. after' designs (without an untreated comparison group), and cross-sectional regressions. Data Collection and Analysis: Descriptive characteristics and statistical information in included studies will be coded and checked by at least two members of the review team. Studies selected for the review will be assessed for confidence in the findings using a critical appraisal tool for determining confidence in primary studies. Standardised effect sizes will be calculated and, if a study does not provide sufficient raw data for the calculation of an effect size, we will attempt to contact the author(s) to obtain this data. We will aim to use random-effects meta-analysis and robust-variance estimation procedures to synthesise effect sizes. If a study includes multiple effects, we will carry out a critical assessment to determine (even if only theoretically) whether the effects are likely to be dependent. Where we suspect dependent effects, we will determine whether we can account for these by robust variance estimation. We will explore the moderating influence of participant and study characteristics, such as gender, race, substances targeted and length of follow-up. Where effect sizes are converted from a binary to continuous measure (or vice versa), we will undertake a sensitivity analysis to investigate the effect of the inclusion of studies with a converted effect size in the meta-analysis by running an additional analysis with these studies omitted. We will also assess the sensitivity of results to inclusion of non-randomised studies and studies classified as low confidence in findings. All analyses will include an assessment of statistical heterogeneity. Finally, we will undertake analysis to assess whether publication bias is likely to be a factor in our findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".