Quality leadership, technology integration and patient care quality across countries: moderating roles of national culture and infrastructure development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose To better understand the nature and effective delivery of quality health-care globally, this paper aims to study the role of quality leadership on patient care quality (PCQ) delivered in hospitals, including the intervening role of technology integration and two country-level factors – national culture and infrastructure development – in North America (Canada, Mexico and the USA). Design/methodology/approach PCQ comprises four facets: interpersonal, technical, environmental and administrative quality. Using survey data and interdisciplinary theoretical support (e.g. quality management and the Global Leadership and Organizational Behavior Effectiveness Project [GLOBE] model of national culture), this paper tested for moderated mediation between hospital quality leadership and the four-facet PCQ model with technology integration as the mediator and national culture and infrastructure development as moderators. Findings Results show that technology integration partially mediates the relationship between hospital quality leadership and PCQ and that national culture and infrastructure development shape the role of hospital quality leadership on PCQ. Hence, these national factors must be considered holistically to understand the impact of hospital quality leadership on patient care. Practical implications To improve PCQ, hospital leaders should broaden their understanding of quality health-care to include technology integration and an awareness of cultural and institutional differences across nations. Originality/value This paper used primary data from hospital quality leaders and the four-facet PCQ conceptualization across three large North American nations, offering a more global understanding of service quality in health-care.
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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 | 0.001 |
| 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 it