EXPLORING PATIENTS PERCEPTIONS OF THEIR SURGEON BASED ON ATTIRE
Bibliographic record
Abstract
Background: Physician attire is an important factor in the patient’s first impression of their doctor. The purpose of this study is to determine how different forms of attire impact patient perceptions of their physicians within our orthopaedic clinics.Methods: A survey was distributed to new patients visiting an orthopedic surgery clinic within a 9 month span at a Canadian outpatient hospital. Each participant also received either a male or female photo sheet depicting 6 different forms of physician attire: Surgical scrubs and white coat, surgical scrubs alone, formal wear with white coat, formal wear alone, business suit and casual wear. Demographic data and general questions related to surgeon’s attire as well as specific questions pertaining to the pictures provided were collected.Results: 100 patients responded to the survey. Most respondents agreed that physician attire was important and they expected their surgeon to be dressed professionally. Respondents felt strongly that there was an association between how a physician dressed and their perceived ability to dispense care. There was a significant preference for the surgeons wearing a white coat. The least favored surgeon attire overall was casual wear.Discussion: The results from our survey identify the importance of surgeon’s attire in the patient’s perception of their surgeon as a health care provider. Attire was identified as influencing patient confidence and possible likelihood of compliance/follow-up.Conclusion: We have identified the white coat as being an important adjunct to the surgeon’s attire that embodies professionalism and inspires confidence in a surgeon.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".