Identification of Patient-Perceived Barriers to Communication between Patients and Physicians
Bibliographic record
Abstract
Purpose: Barriers to full disclosure and communication of complete and accurate health history from patients to their physicians can compromise patient care. Identification of barriers to communication between patients and their physicians, and assessing communication techniques to overcome putative barriers may improve medical training, quality patient care, and patient experience. Methods: The authors performed a cross-sectional study using a novel questionnaire at an urban, inner-city hospital in Toronto, Ontario between January 1, 2011 and January 1, 2012, in order to evaluate potential barriers to communication. Variables included physician age, gender, education, ethnicity, position, perceived sexual orientation, marital status, physical attractiveness and reason for appointment. All patients attending a gynaecology appointment received the paper-based, anonymous questionnaire. Analyses applied the statistical package, SAS Software, Version 9.2. Results: Responses for 286 completed questionnaires were analysed. The most common barriers to communication included having a male physician (40.9%) and having a history taken by a medical student (24.5%). Sensitivity to having a male provider was more frequently reported in women under the age of thirty (63.6%) and nulliparous women (49.6%), p<0.05. Communication was perceived to be improved when physicians acknowledged patient concerns (95.1%), sought to understand patient concerns (91.9%), and included the patient in decisionmaking (74.1%). Conclusions: Physician gender and education level are barriers to full disclosure and communication from patients. Physicians should strive to understand patient concerns and include patients in decision-making in order to encourage full disclosure. Awareness of these obstacles is vital to promoting patient-centered care and to effective physician training.
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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 | 0.005 | 0.033 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".