Individual-Level Factors are Significantly More Predictive of Employee Innovativeness Than Job-Specific or Organization-Level Factors: Results From a Quantitative Study of Health Professionals
Bibliographic record
Abstract
Individual innovativeness is particularly indispensable among health professionals. The healthcare environment is complex and its knowledge workers must continually adapt to change and be comfortable with ambiguity. The objective of this study was to determine the relative importance of individual, job-specific, and organizational factors on innovative output of health professionals. Employed Canadian Registered Dietitians (n = 237) completed an online survey incorporating relevant validated tools, including the 10-item Big Five Inventory and the Alberta Context Tool. Factors were classified by level and introduced in blocks to a multivariate linear regression model, with the outcome of self-reported innovative output. Factors included in the model explained 44% of variation in self-reported innovative output. Although all blocks contributed significantly to the model, minimal variation was explained by factors at the job-specific (4%) and organizational levels (4%). Factors at the individual level most predictive of innovative output were role innovation, the personality trait of conscientiousness and voluntary membership in a professional association. To encourage employee innovativeness, health administrators, and managers of health professionals should consider how best to incorporate screens for individual-level indicators of innovative output (eg, personality tests) in their institutional hiring and selection processes.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".