Mental Illness Treatment Non-Adherence: A Perpetuating Factor of Homelessness among Indigenous People
Bibliographic record
Abstract
Mental illness correlates with homelessness, and a vicious cycle exists between the two. Breaking this vicious cycle will entail propagating effective interventional mental illness treatment modalities which need to be adhered to by the patients. Non-adherence to mental illness treatment, even if socio-economic supports were provided, perpetuates homelessness. Homelessness among indigenous people is higher when compared to non-indigenous people in countries like Canada, Australia, New Zealand, and the United States. This study aims to look at the extent to which non-adherence to mental illness treatment perpetuates homelessness and also the socio-cultural, medical practice, and policy implications. A retrospective literature review was carried out, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Relevant articles were sourced from the PubMed, Google Scholar, and Cochrane Systematic Review databases. The Medical Subject Heading (MeSH) thesaurus was employed to identify relevant concepts. The Boolean method was used to combine the keywords to create a uniform search for articles across the databases. Included articles were free full texts published between 2003 and 2023 in the English language. Fifty-three articles were obtained, and the information obtained confirmed that non-adherence to mental illness treatment would impede recovery and perpetuate homelessness. This article developed a graphical illustration of the homelessness – mental illness vicious cycle and the adjacent mental illness treatment non-adherence and adherence pathways. This illustration could be useful for future studies to better conceptualize mental illness engendered homelessness and the interactions between medical treatment and other variables like housing and intergenerational trauma. This study concludes and recommends that indigenous people-centred policies and Interventional approaches that take the indigenous people’s sensitivities and proclivities should be formulated, propagated, and constantly reviewed to address perpetual homelessness. It is recommended that healthcare practitioners should be aware of and respect these socio-cultural sensitivities and proclivities.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".