How do Patient Care Quality Facets Differ Across North America?
Bibliographic record
Abstract
It is unclear whether patient care quality (PCQ), which comprises four facets—interpersonal, technical, environmental, and administrative quality—differs across hospitals in the three contiguous countries of North America—the US, Canada and Mexico. To offer a more nuanced understanding of the comprehensive nature of PCQ and the roles of their antecedents, we disaggregated the four PCQ facets. Using a mix of primary and secondary data drawn from hospital quality experts in the three nations wee empirically tested a model whereby two country-level factors—national culture and a country’s level of infrastructure development—moderate the roles of hospital quality leadership and technology integration on each of the four PCQ facets. The results support a negative moderation by infrastructure on the positive role of a hospital’s quality leadership on environmental quality. This study contributes to healthcare operations literature by highlighting the important role of a country’ institutional attributes on PCQ delivery, as well as the role of quality leadership in this process. We contribute to medical practice in hospitals as well. Given the increase in globalization, travel and migration among healthcare workers and the general population across North America, our results imply that physician and nursing staff should be sensitized to cultural and institutional differences in healthcare stakeholder definitions of quality care. It would improve hospitals’ ability to provide care for all patients thereby globalizing healthcare.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".