Environment for innovation: exploring associations with individual disposition toward change, organizational conflict, justice and trust
Bibliographic record
Abstract
The environment in higher education and healthcare is rapidly changing. Adaptation through innovation is critical for organizations responsible for the education of healthcare providers. This study examined the climate for innovation at chiropractic colleges and health sciences universities offering a doctor of chiropractic program. The relationship between conflict, resistance to change, trust, justice, and climate for innovation was explored. Prior research involving these variables at chiropractic colleges or health sciences universities offering a doctor of chiropractic program is extremely limited. The chiropractic profession is experiencing significant challenges including market competition, internal conflict, and significant healthcare policy change. Adding to the dilemma, enrollment at chiropractic colleges has experienced a substantial decline over the past decade.\nThis study surveyed faculty at all chiropractic colleges and health science universities offering a doctor of chiropractic program in the United States and Canadian Memorial Chiropractic College. A response rate of nearly 50% was achieved. Data analysis showed higher levels of organizational trust and justice were associated with higher perceptions of climate for innovation. The relationship between justice and climate for innovation was not mediated by trust. Additionally, higher levels of resistance to change and increased conflict between administration and faculty were associated with lower perceptions of climate for innovation. The study findings offer insight for chiropractic colleges and health sciences universities offering a doctor of chiropractic program wishing to promote innovation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".