Public Health Care Policies and Their Impact on Patient Satisfaction
Bibliographic record
Abstract
Health care is a trillion-dollar industry, but without public policies in place to support a sustainable health care system, life would decline. For instance, health care providers now receive decreased payments from federal agencies if their health facility scores do not meet national benchmarks. The purpose of this retrospective, quantitative, comparative study was to examine the extent to which patient satisfaction was impacted by national public health policies in the United States and Canada. The research questions related to how health care reimbursement policies and patients’ financial responsibility of both the eastern United States and eastern Canada would predict country-specific patient satisfaction scores for people 55 to 75 who had a medical procedure in the past 2 years. The independent variables were public policy and financial responsibility and the dependent variable was patient satisfaction. Linear regression only slightly validated the original hypotheses, so logistic regression was utilized for a more detailed interpretation. Using logistic regression analyses with 164 participants, higher satisfaction scores predicted higher satisfaction in the United States (B = 1.95, Wald[1] = 13.47, p < .001) based on shorter wait times for medical procedures and obtaining results, and higher satisfaction scores in Canada (B = 1.94, Wald[1] = 13.60, p < .001) based on the reduced cost associated with medical treatments. The results of this study may be applicable to other locations that face health care reform challenges, promoting positive social change for patients seeking better satisfaction with their health care services.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".