Factors that Impact Patient Satisfaction and Perceptions in Patient-Physician Interactions in Canada: A Literature Review
Bibliographic record
Abstract
Introduction: With a publicly funded health care system, the drivers and practices to measure quality of care and patient satisfaction in Canada are unique. This study attempts to investigate the current factors influencing patient satisfaction and perceptions of patient-physician interactions, specifically in Canada. Methods: A literature review of the existing studies was conducted by searching through five databases including Scopus, Pubmed, Web of Science, JSTOR, and ProQuest. After the input of a consistent list of keywords into each database, records were screened and assessed for eligibility. The final chosen articles were analyzed for trends and inconsistencies. Results: Twelve articles were eligible and selected for the final analysis. The factors covered in these articles covered themes such as the digitalization of healthcare, time constraints, patient attributes, and physician attributes. Most studies were qualitative in nature, providing little to no correlational quantitative findings. Discussion: A physician’s attitude towards computer use has been positively correlated with the patient’s preference of physicians using computers in the office. Time constraints were not correlated with patient perceptions of quality of care and interactions with their emergency physicians in a statistically significant manner. Physician general communication behaviors such as answering questions and a caring attitude have been positively correlated with patient perceptions of their physician’s end-of-life communication skills. All other factors mentioned in the results were only described in qualitative interviews. Conclusion: Overall, future studies should focus on replicating the existing quantitative studies in different Canadian provinces and across different medical specialties. In addition, qualitatively gathered potential factors should be examined in a more structured and statistically supported manner. The existing factors and those explored in future studies can offer an opportunity for the improvement of quality of care and health outcomes in Canada.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.059 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".