A Qualitative Meta-Synthesis Using Meta-Ethnography for Psychosocial Interventions with People Experiencing Homelessness
Bibliographic record
Abstract
Introduction: Homelessness is a social and health issue caused by various factors, including, but not limited to, mental and physical health challenges, unemployment, family disruption, and substance use. The inability to permanently exit homelessness and maintain housing can be attributed to a lack of support for those with higher needs. This is where psychosocial interventions can be vital in supporting re-housing and housing stability. Method: We conducted a qualitative meta-synthesis of psychosocial interventions supporting people experiencing homelessness, aiming to identify and synthesize qualitative studies about psychosocial interventions in terms of their overall impact. These interventions include peer support, Critical Time Intervention (CTI), Assertive Community Treatment (ACT), Dialectical Behavioral Therapy (DBT), Cognitive behavioural therapy (CBT), housing support, and case management. Result: We have included 54 articles meeting the study criteria and presented the meta-ethnographic meanings of the work within four broad themes, the first three being analytic and the fourth being descriptive: 1) Even If Brief, These Do Not Disappoint, 2) Mental Journey: Transforming from Surviving to Thriving, 3) Housing Journey: The Way In is the Way Out, and 4) Elements of Effective Psychosocial Interventions. Conclusion: From this review, we have noted that the ‘supports’ in ‘housing with supports’, or ‘supports to exit homelessness’, involve individual-level psychosocial interventions. Our study illustrates that individual factors and experiences of support are part of an effective system, bridging the micro and the macro to end the issues of homelessness.
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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 | 0.087 | 0.163 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".