Developing and implementing an action-oriented staff survey: Queensland Health and the 'Better Workplaces' initiative
Bibliographic record
Abstract
QUEENSLAND HEALTH IMPLEMENTED the ?Better Workplaces? staff opinion survey (the survey) in May 2006. The initiative stands as the largest single staff survey ever conducted in Queensland, and one of the largest in Australia. This case study outlines the process of this project, the outcomes to date and some of the pitfalls and successes along the way. Logistically it involved 37 health service districts and 10 corporate areas spread across the state. The survey process incorporated four survey periods over two years. The aim of the survey was: to improve workplace culture at the local level and across the organisation as a whole. Workplace culture is defined by Cole as ?The collection of unwritten rules, codes of behaviour and norms by which people operate, how we do things around here?1 Queensland Health proposed to improve its workplace culture by listening to staff and developing and driving targeted action plans following the survey with each district and division to create a climate of trust, respect, and innovation among staff which will ultimately improve patient outcomes. ?. . . The creation of a culture that is free of blame and encourages an open examination of error and failure is a key feature of services dedicated to quality improvement and to learning.?
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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 | 0.091 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".