Healthcare technologies, quality improvement programs and hospital organizational culture in Canadian hospitals
Bibliographic record
Abstract
BACKGROUND: Healthcare technology and quality improvement programs have been identified as a means to influence healthcare costs and healthcare quality in Canada. This study seeks to identify whether the ability to implement healthcare technology by a hospital was related to usage of quality improvement programs within the hospital and whether the culture within a hospital plays a role in the adoption of quality improvement programs. METHODS: A cross-sectional study of Canadian hospitals was conducted in 2010. The sample consisted of hospital administrators that were selected by provincial review boards. The questionnaire consisted of 3 sections: 20 healthcare technology items, 16 quality improvement program items and 63 culture items. RESULTS: Rasch model analysis revealed that a hierarchy existed among the healthcare technologies based upon the difficulty of implementation. The results also showed a significant relationship existed between the ability to implement healthcare technologies and the number of quality improvement programs adopted. In addition, culture within a hospital served a mediating role in quality improvement programs adoption. CONCLUSIONS: Healthcare technologies each have different levels of difficulty. As a consequence, hospitals need to understand their current level of capability before selecting a particular technology in order to assess the level of resources needed. Further the usage of quality improvement programs is related to the ability to implement technology and the culture within a hospital.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".