Measuring the patient experience in primary care: Comparing e-mail and waiting room survey delivery in a family health team.
Bibliographic record
Abstract
OBJECTIVE: To compare the characteristics and responses of patients completing a patient experience survey accessed online after e-mail notification or delivered in the waiting room using tablet computers. DESIGN: Cross-sectional comparison of 2 methods of delivering a patient experience survey. SETTING: A large family health team in Toronto, Ont. PARTICIPANTS: Family practice patients aged 18 or older who completed an e-mail survey between January and June 2014 (N = 587) or who completed the survey in the waiting room in July and August 2014 (N = 592). MAIN OUTCOME MEASURES: Comparison of respondent demographic characteristics and responses to questions related to access and patient-centredness. RESULTS: Patients responding to the e-mail survey were more likely to live in higher-income neighbourhoods (P = .0002), be between the ages of 35 and 64 (P = .0147), and be female (P = .0434) compared with those responding to the waiting room survey; there were no significant differences related to self-rated health. The differences in neighbourhood income were noted despite minimal differences between patients with and without e-mail addresses included in their medical records. There were few differences in responses to the survey questions between the 2 survey methods and any differences were explained by the underlying differences in patient demographic characteristics. CONCLUSION: Our findings suggest that respondent demographic characteristics might differ depending on the method of survey delivery, and these differences might affect survey responses. Methods of delivering patient experience surveys that require electronic literacy might underrepresent patients living in low-income neighbourhoods. Practices should consider evaluating for nonresponse bias and adjusting for patient demographic characteristics when interpreting survey results. Further research is needed to understand how primary care practices can optimize electronic survey delivery methods to survey a representative sample of patients.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".