Stay Connected. Manage Your Care.
Access your health information anytime and anywhere, at home or on the go, with MyHealth.
- Message your clinic
- View your lab results
- Schedule your next appointment
- Pay your bill
The MyHealth mobile app from Stanford Health Care puts all your health information at your fingertips and makes managing your health care simple and quick.
Guest Services
24/7
We are available to assist you
whenever you need it. Give us a call at
650-498-3333 or
PHYSICIAN HELPLINE
Have a question? We're here to help! Call 1-866-742-4811
Monday - Friday, 8 a.m. - 5 p.m.
REFER A PATIENT
Fax 650-320-9443
Track your patients' progress and communicate with Stanford providers conveniently and securely.
Abstract
A patient's response to treatment may be influenced by the expectations that the patient has before initiating treatment. In the context of clinical trials, the influence of participant expectancy may blur the distinction between real and sham treatments, reducing statistical power to detect specific treatment effects. There is therefore a need for a tool that prospectively predicts expectancy effects on treatment outcomes across a wide range of treatment modalities.To help assess expectancy effects, we created the Stanford Expectations of Treatment Scale (SETS): an instrument for measuring positive and negative treatment expectancies. Internal reliability of the instrument was tested in Study 1. Criterion validity of the instrument (convergent, discriminant, and predictive) was assessed in Studies 2 and 3.The instrument was developed using 200 participants in Study 1. Reliability and validity assessments were made with an additional 423 participants in Studies 2 and 3.The final six-item SETS contains two subscales: positive expectancy (? = 0.81-0.88) and negative expectancy (? = 0.81-0.86). The subscales predict a significant amount of outcome variance (between 12% and 18%) in patients receiving surgical and pain interventions. The SETS is simple to administer, score, and interpret.The SETS may be used in clinical trials to improve statistical sensitivity for detecting treatment differences or in clinical settings to identify patients with poor treatment expectancies.
View details for DOI 10.1177/1740774512465064
View details for Web of Science ID 000312452600015
View details for PubMedID 23169874