Patient preferences for chiropractors’ attire: a cross-sectional study of UQTR university-based chiropractic clinic
Bibliographic record
Abstract
BACKGROUND: A significant body of research has examined how the attire of physicians and nurses affects patients' perceptions, preferences, and outcomes. However, limited research has focused on the clothing worn by other health professionals, such as chiropractors. The present study aims to explore patients' preferences and perceptions of chiropractors' attire. METHODS: Using a cross-sectional image-based procedure, new patients to a university clinic were questioned regarding their preferences for four different attires (casual, formal, scrub, and white coat) worn by both a male and a female chiropractor. Patients also reported their perceptions in terms of chiropractors' knowledge, trustworthiness, competence, professionalism, and comfortable for each photograph. RESULTS: From August 10, 2022, to January 23, 2023, 75 new patients participated in the study. Results indicated a strong preference for scrubs for both male and female chiropractors. Chiropractors in scrubs were also seen as more knowledgeable, trustworthy, competent, and professional, and comfortable. This was closely followed by those wearing white coats and formal attire. Notably, the white coat worn by the female chiropractor received significantly more positive ratings than when worn by her male counterpart. CONCLUSION: In conclusion, our findings suggest that chiropractors' attire influences patients' perceptions and should be considered in the development of dress codes for public and private clinics. Further research is essential to understand better how the gender and age of care providers affect patient evaluations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".