Readiness for organizational change: A longitudinal study of workplace, psychological and behavioural correlates
Bibliographic record
Abstract
To examine factors influencing readiness for healthcare organizational change, 654 randomly selected hospital staff completed questionnaires measuring the logistical and occupational risks of change, ability to cope with change and to solve jobrelated problems, social support, measures of Karasek's (1979) active vs. passive job construct (job demand× decision latitude) and readiness for organizational change. Workers in active jobs (Karasek, 1979) which afforded higher decision latitude and control over challenging tasks reported a higher readiness for organizational change scores. Workers with an active approach to job problem‐solving with higher job change self‐efficacy scores reported a higher readiness for change. In hierarchical regression analyses, active jobs, an active job problem‐solving style and job‐change self‐efficacy contributed independently to the prediction of readiness for organizational change. Time 1 readiness for organizational change scores and an active approach to job problem‐solving were the best predictors of participation in redesign activities during a year‐long re‐engineering programme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".