A Comparison between Generation X and Generation Y in Terms of Individual Innovativeness Behavior: The Case of Turkish Health Professionals
Bibliographic record
Abstract
Today the criterion of innovation has become a paradigm for all institutions to maintain their success. Innovation is essential for service industry as well as production industry. It is the individual that is in the center of innovation, which can be defined as the difference between a good idea and a good product. The innovative work behavior of individuals is an area that needs to be scrutinized in terms of innovativeness. Because innovation directly effects the quality of life, it has a significant role in the health sector.In this perspective, this study aims to identify the innovativeness level of individuals working in health industry where innovation is of significance; to identify the differences of innovativeness levels between generations; and to identify the relationship between individuals’ perception of whether the institution they work have learning orientation and their level of innovativeness.The data in this study, which specifically addresses health institutions with their increasing role in the information society, were gathered from the three private hospitals in Black Sea Region in Turkey. 274 health professionals working in those three private hospitals and accepting to take part in the study formed the sample group of the study.The results of the study show that there is a difference of innovative behavior scores between the participants coming from Generation X and Y. This difference indicates that Generation X is more innovative. Moreover, majority of the participants from Generation X are physicians. In this respect, it can be noted that in terms of innovative behavior the difference between physicians and other health professionals (professional difference) is more significant than the difference between generations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".