The impact of wearing a white coat on the perception of older people
Bibliographic record
Abstract
Objective: Previous studies have demonstrated that wearing a white coat affects patients (“the white coat effect”), the individual wearing the white coat (“enclothed cognition”), and the relationship itself between both parties. The aim of our study is to determine whether our perception of an older person differs when they are interacting with a professional caregiver wearing a white coat as opposed to when the caregiver is in civilian clothing. To the best of our knowledge, no study has been conducted on this subject thus far.Methods: In this cross-sectional study, we recorded two videos showing an older person with a professional caregiver. The videos are identical except for the caregiver's attire: white coat vs. civilian clothing. 135 volunteers from the general population took part in our online survey and watched one of the two videos. Then, the perception of the older person was evaluated with 10 pairs of opposing adjectives (such as: “independent/dependent”). Participants were asked to move the cursor between the two adjectives. Multiple regression analyses were conducted to compare the perceptions both groups.Results: The results obtained indicate that when the caregiver is wearing a white coat, the older person at their side is perceived as significantly (14.77%) more dependent as opposed to when the caregiver is in civilian clothing. The caregiver is also perceived as significantly more competent when wearing a white coat.Conclusions: Professional caregiver’s wearing a white coat is likely to have an impact on the perception of the older people in contact with said caregivers. Older people may be perceived as more dependent if the nursing staff (at home, in nursing home) wear white coats.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".