A scoping review of psychiatric conditions associated with chronic pain in the homeless and marginally housed population
Bibliographic record
Abstract
The present review sought to examine and summarise the unique experience of concurrent pain and psychiatric conditions, that is often neglected, within the population of homeless individuals. Furthermore, the review examined factors that work to aggravate pain and those that have been shown to improve pain management. Electronic databases (MEDLINE, EMBASE, psycINFO, and Web of Science) and the grey literature (Google Scholar) were searched. Two reviewers independently screened and assessed all literature. The PHO MetaQAT was used to appraise quality of all studies included. Fifty-seven studies were included in this scoping review, with most of the research being based in the United States of America. Several interacting factors were found to exacerbate reported pain, as well as severely affect other crucial aspects of life that correlate directly with health, within the homeless population. Notable factors included drug use as a coping mechanism for pain, as well as opioid use preceding pain; financial issues; transportation problems; stigma; and various psychiatric disorders, such as post-traumatic stress disorder, depression, and anxiety. Important pain management strategies included cannabis use, Accelerated Resolution Therapy for treating trauma, and acupuncture. The homeless population experiences multiple barriers which work to further impact their experience with pain and psychiatric conditions. Psychiatric conditions impact pain experience and can work to intensify already adverse health circumstances of homeless individuals.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".