Australian doctors are more engaged than UK doctors: why is this the case?
Bibliographic record
Abstract
BACKGROUND: Despite reports highlighting the need for greater medical engagement and the benefits of being widely understood, very little information is available on the status of medical engagement in Australia, and how this compares to the UK. Answering this question will no doubt assist training bodies, curriculum designers and policy makers better understand relevant issues. METHODS: The medical engagement questionnaire (MES) was emailed to all medical staff working at 159 UK National Health Service Trusts and 18 health service organisations in Australia. The questionnaire consists of 30 predetermined items seeking responses using a 5-point Likert scale. RESULTS: Overall, doctors in the Australian dataset are slightly more engaged, or more positive, than their UK colleagues. Good interpersonal relationships was the only variable that UK doctors scored more positively than their Australian counterparts. At the lower end of the responses, that is the least engaged, we found this even more apparent. Where doctors in Australia are less disengaged, that is still more positive than the UK colleagues. CONCLUSION: While the profiles of medical engagement vary at the sites and also across the MES and subscales, the data illustrate that overall doctors in Australia feel valued and empowered, and they have purpose and direction and work in a collaborate culture. At the most disengaged end of the scale, Australian doctors are markedly less disengaged than their UK counterparts. There may be numerous factors that influence and change how engaged doctors are in both countries. The most prominent of these are appear to be working conditions and lifestyle, driven by funding and other economics issues. This research is likely to be of great interest to regulators and training bodies in both countries.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".