Complementary medicine among individuals experiencing homelessness in Switzerland: a quantitative and qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: People experiencing homelessness (PEH) are disproportionately affected by health issues yet remain underserved by the health care system. Emerging findings suggest that complementary medicine (CM) approaches might help address the low access to earlier treatment and the complex needs of this population. Very little research has explored this topic in Europe. Thus, this study aimed to explore perceptions, experiences, and interests in CM among PEH in Switzerland. METHODS: Participants (N = 123) were PEH in French-speaking Switzerland who completed a questionnaire assessing their use of and interest in CM. A subsample of the survey participants (n = 18) and 14 professionals working in the homeless-serving sector participated in semi-structured interviews exploring perceived utility of CM for PEH. Descriptive statistics and conventional content analysis were used to analyze quantitative and qualitative data, respectively. RESULTS: Quantitative findings showed that despite high levels of interest in CM, less than 30% of participants reported using CM at least once in the previous 6 months. The five CM modalities with the highest interest were osteopathy (61.5% interested or very interested), therapeutic massage (59.2%), nutritional supplements (57.1%), music therapy (50.8%), and acupuncture (49.1%). The qualitative findings reinforced the substantial interest in CM. They revealed that CM approaches were perceived as useful to mitigate some health and social issues frequently encountered in this population, ultimately contributing to improved health and well-being. Participants made recommendations for practices that would help engage and retain PEH in a CM program, such as making it accessible and equitable, and following certain steps to earn the trust of PEH. Finally, a minority of participants questioned the relevance of a CM program for this population, arguing instead that more urgent social and conventional medical needs must be addressed first. CONCLUSIONS: Taken together, these findings suggest that integrative medicine, which incorporates conventional and complementary approaches to medical and social services may represent a suitable offering for PEH to address their competing bio-psycho-social needs. Using a community-based participatory approach to co-develop such a program might help to ensure effectiveness and thereby increase health equity.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".