Bibliographic record
Abstract
Objective This research explores measures of employee engagement in the National Health Service (NHS) acute Trusts in England and examines the association between organisation-level engagement scores and quality ratings by the Care Quality Commission (CQC). Design Cross-sectional. Setting 97 acute NHS Trusts in England. Participants 97 NHS acute Trusts in England (2012–2016). Data include provider details, staff survey results and CQC reports. Hybrid Trusts or organisations affected by recent mergers are excluded. Outcome measures Analysis uses organisation-level employee engagement and CQC quality ratings. Results Employee engagement is affected by organisational factors, including patient bed numbers (β=−0.46, p<0.05) and financial revenue (β=0.38, p<0.05). CQC ratings are predicted by overall employee engagement score (β=0.57, p<0.001) and financial deficit (β=−0.19, p<0.05). The most influential employee engagement dimension on provider ratings is ‘advocacy’ (λ=0.54, p<0.001). Analysis supports the notion that employee engagement can be predicted from advocacy scores alone (eigenvalue=4.03). Better still, combining advocacy scores from the previous year’s survey or adding in motivation scores is a highly reliable indication of overall employee engagement (95.4% of total variance). Conclusions NHS acute Trusts with high employee engagement scores tend to have better CQC ratings. Trusts with a high financial deficit tend to have lower ratings. Employee engagement subdimensions have different associations with CQC ratings, the most influential dimension being advocacy score. A two subdimension model of engagement efficiently predicts overall employee engagement in NHS acute Trusts in England. Healthcare leaders should pay close attention to the proportion of employees who would recommend their organisation as a place to work or receive treatment, because this is a proxy for the level of engagement, and it predicts CQC ratings.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".